No, AI and Big Data Are Not Going to Win the Next Great Power Competition – The Defense Post

Artificial Intelligence and Big Data, two buzzwords that are colloquially interchangeable but subtly nuanced, are not silver bullets poised to handily solve all of the US militarys problems.

Unpopular opinion: the US military and the defense industrial complex are currently giving up one heck of a run to the Chinese Communist Party. Note how I didnt say that were losing yet.

This century saw a solid first quarter, with US domination that witnessed the rise and fall of a competing Soviet Union and the establishment of global American hegemony both militarily and economically. We have enjoyed decades of unipolar dominance. When youre on top, it typically feels like you could never lose.

However, the rapidly shifting political landscape and return to great power competition have America reeling. The Chinese Communist Party and its Peoples Liberation Army are mounting a comeback.

While we were worried about cracking terrorist networks of bearded men with AK-47s in caves, the Chinese were speeding towards advanced technologies, hypersonic weapons, and the very defenses required to put up a front against the predictable American military machine.

The Chinese have seized this strategic opportunity. While the West was distracted, Beijing sunk billions of dollars into anti-access and area denial capabilities (A2/AD), a defensive posture aimed at the American way of fighting. They have also amassed massive amounts of data required to weaponize and harness the benefits of Big Data.

Chinese tech companies and government-sponsored research initiatives have built massive data sets while we were preoccupied with Iraq and Afghanistan. These are precisely the requisite data sets necessary to train Machine Learning algorithms and AI neural networks.

While we were building social networks by word of mouth of terrorist cells, the Chinese were collecting intelligence and building advanced systems for moving data with little regard for civil liberties, privacy, or data protection. Not that I advocate for it, but it is amazing what you can do when you ignore ethics or societal norms.

In defense tech news, all I read about is AI solving the joint, all-domain command and control problem, or Big Data providing a potential solution for some multi-domain capability gap. Perhaps we just desire an easy, one size fits all solution in the form of a Big Data Band-Aid?

Indeed, it seems like our greatest adversary and the second greatest existential threat to the American way of life after nuclear war has already found the elixir of life in Big Data, so why cant we?

For starters, artificial intelligence is not the Terminator. It is not a killing machine that is easily weaponized, deployed, and employed to combat adversary capabilities. Even the most cutting edge artificial intelligence tools today are narrow in scope and limited in application.

While that will change eventually, algorithms are currently fantastic for vehicle routing, search engine optimization, facial recognition, asking Siri to set a timer, and other modern technical conveniences that we all carry around in our pockets. These are simple applications of AI. These are not weaponized, military applications that result in warheads on foreheads or power projection.

AI is great at parsing through billions of bits quickly and making sense of it all; creating information from data is its strong suit. This is not complicated. At their core, these algorithms rely on data configuration and formatting to sort and shape vast matrices full of different variables, perform some sort of reduction or matrix operation, and compare this reduction to a set of user-programmed decision criteria.

There is a difference between artificial intelligence and decision making. AI facilitates expedited data to decision throughput, but it does not make its own decisions in a vacuum.

Next, AI is slightly more complicated versions of the matrix math you were probably introduced to in algebra. This advanced linear algebra is advanced applied statistics. By itself, it does not result in a major weapons system delivering effects against an adversary position. Just like space and cyber effects at your favorite large force exercise, you cannot simply sprinkle some Big Data on top and bring added military capability to bear to win the 21st-century fight.

On their own, AI and Big Data do not result in increased competition by the US military. They dont produce a capability to which the Chinese Communist Party has no solution. While they can expedite paths through a particular kill-web to deliver effects, they arent a standalone military capability.

Another reason why AI and Big Data wont solve the A2/AD problem is because of the laws of physics. The US Indo-Pacific Command Area of Responsibility poses a geography problem for the US military. It requires ships and airplanes to travel farther to even get to the fight. Missiles can only go so far and fast, and AI does not provide a solution that creates a silver bullet hypersonic solution.

A2/AD is also a logistics nightmare. Posturing the supplies and equipment at disparate operating locations anywhere from the Philippines to Guam or to Alaska to support even a limited regional conflict is a hard nut to crack, and AI does not by itself solve the agile, global logistics problem.

I might sound exceptionally contrarian in my simplification of AI and Big Data. In truth, Im a huge proponent of defense applications for AI and Big Data. Our militarys future hinges on it.

For the Department of Defense (DoD) to harness AI and to weaponize Big Data, the US military machine and industrial base need to integrate artificial intelligence into military systems.

The current generation of developmental systems needs to bake in advanced algorithms to take the human brain as a data filter out of the loop while introducing fusion, machine/deep learning, and the power of computation to military applications.

The old way of filtering data and enabling the military operators tactical decision making is irrelevant today. If the DoD cant shift, adapt, and embrace this change, theyre doomed to fight the last war for the rest of this century.

The DoD, like many contemporary large organizations, will face many hurdles in weaponizing artificial intelligence capabilities.

One of the main challenges in this transition is simple integration. Thats something the DoD already isnt good at. To abuse an overused example, the F-22 and F-35, arguably the worlds most advanced tactical fighters, cannot communicate via their tactical data links. While they were both developed by Lockheed Martin, their data links use different standards for their waveforms and are not interoperable. To oversimplify two prodigiously complex weapon systems, the F-22 is using AM radio and the F-35 uses FM.

This is partially the governments fault but also the fault of the big defense contractors. Back to my data link example: in the 21st century, these capabilities are software-driven. However, major defense contractors are hardware companies.

During the early years of the American century, they mobilized and bent metal to create some of the last generations most capable machines. That said, they have a comparative advantage only in producing hardware, not in the software required to fight in the 21st century.

For the DoD to be successful in harnessing AI for the next conflict, it needs to foster relationships with organizations that operate in the tech space with a mastery of software development featuring AI applications.

The key is integrating AI and Big Data capabilities into military applications of all kinds, across the full spectrum of military operations. Global logistics, command and control, persistent ISR, and advanced weapons are untapped applications for AI that have not yet been touched by the tech space.

The traditional bloated defense contractor is not resourced for this, nor do they have the right skill sets. Only seasoned developers outside the typical defense industrial base have the know-how to actually succeed with this integration.

AI alone wont compete with Chinese military capabilities. Applying the tenets of big data and weaponizing it to field advanced and lethal military capabilities is the future of power competition.

The Chinese are catching up and may one day challenge American global military dominance, but applied AI capabilities and advanced data science just might be the key to preserving American hegemony and protecting American interests domestically and abroad.

Alex Hillman is an analyst and engineer in the defense space. A US Air Force Academy graduate, he holdsmasters degrees in operations research, systems engineering, and flight test engineering, and has previously served in various technical and leadership roles for the USAF. Alex is a graduate of the United States Air Force Test Pilot School and a former US Department of State Critical Language Scholar for Russian.

Disclaimer: The views and opinions expressed here are those of the author and do not necessarily reflect the editorial position of The Defense Post.

The Defense Post aims to publish a wide range of high-quality opinion and analysis from a diverse array of people do you want to send us yours?Click hereto submit an op-ed.

Original post:

No, AI and Big Data Are Not Going to Win the Next Great Power Competition - The Defense Post

Posted in Ai

Artificial intelligence: A round-up of products with AI as a central function – IBC365

Initially focused on automating repetitive tasks, the integration of Artificial intelligence (AI) and machine learning (ML) is opening the door to many other useful applications. IBC365 takes a look at ten recent product launches and service evolutions, some of which have focused on solving the challenges brought about by the coronavirus health crisis.

1. AI-based ad insertion technologyUK-based Mirriad began using AI technology to digitally insert advertisements and products into movies and TV shows after they have been filmed. As reported by IBC365, Mirriad can digitally embed a branded bottle on a table, a new advertisement on an existing billboard, or a commercial running on the TV in the background. The companys platform uses AI to identify placement opportunities, and then employs visual effects technology to insert real-world objects that werent in the original shoot, or overlays existing brand imagery with new product shots. The idea has already generated attention among broadcasters and advertisers. Indeed, producers have been looking for ways to generate additional revenue from their back catalogues after production stalled during the COVID-19 pandemic.

2. AI in video codecs for optimising video flowsIBC365 also reported on how future advances on optimising video streaming workflows will be made in software that is automated by AI. Companies such as Haivision, Harmonic, InterDigital, iSize Technologies, and V-Nova are working on different ways of applying AI and ML techniques within video codecs.

3. Using AI/ML to convert horizontal to vertical formats for smartphonesFrench news channel BFMTV launched live vertical technology that automatically converts the horizontal frames of standard television streams to a vertical format that is better suited to smartphones. Altice-owned BFMTV collaborated with French start-up Wildmoka to develop the product, which allows the traditional horizontal format of television to be automatically rezoned into a mobile-friendly vertical format using AI and ML techniques. Wildmoka calls its product Auto ReZone, and designed it to provide a mobile-first, vertical viewing experience for news. The product extracts content from live streams or recorded videos, and automatically reconstitutes it into a mobile-first video format (typically 9:16 or 1:1). The tool uses AI and ML to detect all the zones of interest in each 16:9 frames; select a vertical layout/template suitable to fit the various zones of interest detected above; extract each zone from the horizontal frame and adjust them individually to fit the zone sizes of the target vertical layout; and re-compose the extracted zones and graphical elements into the overall final vertical frame.

4. AI in full RDK video-based productSoftAtHome launched a new video-based product based on the Reference Design Kit (RDK) open source software for the video industry, and which uses AI techniques for user-friendly navigation and personal data security. The product integrates multicast, DVB, live DASH streaming, a universal search aggregator, new premium video streaming apps, voice controls, and SoftAtHomes white-label ImpressioTV user interface. The company uses AI algorithms to help optimise the user experience and propose personalised content while keeping data private. In addition, AI-based voice control assets have been integrated into the RDK product to make navigation on TV screens more user-friendly.

5. Using AI and ML to prevent customer churnQligent introduced Foresight as a cloud-based service that uses AI, ML, and big data to mitigate content distribution issues, prevent churn, and protect service provider revenue. Foresight is designed to help broadcasters, MVPDs and OTT service providers understand and correlate factors that contribute to higher audience engagement by providing real-time data analytics based on system performance and user behaviour. The aim is to stop so-called silent sufferers from cancelling their subscriptions by predicting and preventing customer churn. AI and ML provide automated data collection, while deep learning technology mines data from hundreds or thousands of layers of data. Big data technology then correlates and aggregates the data for real-time, cloud-based quality assurance, helping service providers to quickly address distribution issues.

6. AI for managing the connected homeAmdocs launched doxi HomeOS, an AI-based cloud-native home operating system (OS) designed to enable service providers to move beyond basic connectivity services in the connected home. doxi HomeOS provides AI-based insights, simple voice commands and touch-free care capabilities to resolve customer support needs. The OS also offers enhanced cybersecurity monitoring capabilities and parental control over the growing number and usage of connected devices and apps in the home. Furthermore, doxi HomeOS offers consumers the ability to self-manage connectivity and WiFi settings as well as automated, AI-based notifications related to usage patterns and media and gaming consumption. Gil Rosen, general manager of amdocs:next, said doxi HomeOS is relevant for all broadband providers, ranging from incumbents looking to differentiate and grow services to CSPs rolling out 5G fixed wireless access to enhance home broadband connectivity.

7. AI in live transcription servicesEpiphan Video launched LiveScrypt as a live transcription service. LiveScrypt is a cloud and AI-based speech-to-text transcription service that enables audiences to engage with live events as they happen regardless of any hearing impediment, native language, or distraction. LiveScrypt is said to transcribe with at least 85% to 90% accuracy. It also adds punctuation and on-the-fly corrections based on the confidence of words in context. North American Industry Classification System (NAICS) codes for standardised industry-related terms are also supported for greater accuracy.

8. AI in robotic camerasTelemetrics introduced AI techniques as well as motion tracking and servo-mechanical excellence as standard on its latest robotic camera products and systems. For example, the OmniGlide robotic roving platform was improved with new ML algorithms, allowing its shot recall settings to intelligently find the best path within the space in which it is operating. This Path Planning means it can figure out the safest way between point A and point B, even when theres an obstruction (like a news desk) in between. This is accomplished in tandem with the Telemetrics RCCP-2A robotics and camera control panel running STS software.

9. AI for video-on-demand (VoD)SPI/ Film Box launched an AI-based content streaming service called FilmBox Plus. The multi-platform service merges linear and on-demand experiences through AI-supported linear channels and video-on-demand (VoD) content. SPI said the new service is an evolution of FilmBox Live, which has been active for over a decade. It is expected that FilmBox Plus will launch globally by the end of 2020, replacing FilmBox Live.

10. Corporate broadcasting using AI-based vPilotMobile Viewpoint partnered with BuckDesign to provide broadcast studio services, with a specific emphasis on companies looking to enhance their corporate communications and marketing initiatives. BuckDesign is using vPilot technology with AI automation from Mobile Viewpoint as part of its inhouse broadcast studio, which can be rented by companies wishing to deploy their own professional TV broadcast studio. vPilot is an automated studio system that controls multiple cameras without the need for an onsite director or camera operators. BuckDesign, in conjunction with vPilot, built its own studio in Alkmaar in North Holland that is available to corporates wishing to undertake a production without investing in a complete studio. For companies that wish to implement their own studio, BuckDesign can provide the full set-up from room design to implementation of the technology.

See original here:

Artificial intelligence: A round-up of products with AI as a central function - IBC365

Posted in Ai

What Happens When AI is Used to Set Grades? – Harvard Business Review

Executive Summary

In 2020, with high school exams canceled in many countries, the International Baccalaureate Organization (IBO) deployed an AI to determine final grades based on current and historical data. When the results came in, many scores did not correlate with grades that had been predicted, as had been the case in previous years, prompting many people to appeal their grades. Unfortunately, the appeals system for grades had not been changed from previous years, which was assumed that students would write examination papers. Since university place offers in many countries are contingent on students achieving predicted grades, many students have been denied places at their universities of choice, which has resulted in a great deal of anger. This experience highlights the risks of delegating life-altering decisions to AI without considering how apparently anomalous decisions can be appealed and, if necessary, changed.

How would you feel if an algorithm determined where your child went to college?

This year Covid-19 locked down millions of high school seniors and governments around the world canceled year-end graduation exams, forcing examining boards everywhere to consider other ways of setting the final grades that would largely determine the future of the class of 2020. One of these Boards, the International Baccalaureate Organization (IBO), opted for using artificial intelligence (AI) to help set overall scores for high-school graduates based on students past work and other historic data. (We use the term AI broadly to mean a computer program that uses data to execute a task that humans typically perform, in this case processing student scores.)

The experiment was not a success, and thousands of unhappy students and parents have since launched a furious protest campaign. So, what went wrong and what does the experience tell us about the challenges that come with AI-enabled solutions?

The IB is a rigorous and prestigious high-school certificate and diploma program taught by some of the worlds best schools. It opens doors to the worlds leading universities for talented and hard-working students in over 150 countries.

In a normal year, final grades are determined by coursework produced by the students and a final examination administered and corrected by the IBO directly. The coursework counts for some 20-30% of the overall final grade and the exam accounts for the remainder. Prior to the exam, teachers provide predicted grades, which allow universities to offer places conditional on the candidates final grades meeting the predictions. The IBO will also arrange independent grading of samples of each students coursework in order to discourage grade inflation by schools.

The process is generally considered to be a rigorous and well-regarded assessment protocol. The IBO has collected a substantial amount of data about each subject and school hundreds of thousands of data points, in some cases going back over 50 years. Significantly, the relationship between predicted and final grades has been tight. At leading IB schools over 90% of grades have been equal to predicted, and over 95% of total scores have been within a point from that predicted (total scores are set on a scale of one to 45).

In the spring of 2020, IBO had to decide whether to allow the exams to proceed or cancel them and award grades some other way. Allowing exams risked the safety of students and teachers, and could create fairness issues if, for instance, students in some countries were allowed to write the exams at home, while in others they had to sit exams at school.

Canceling the exams raised the question of how to assign grades, and thats when IBO turned to AI. Using its trove of historical data about students course work and predicted grades, as well as the data about the actual grade obtained at exams in previous years, the IBO decided to build a model to calculate an overall score for each student in a sense predicting what the 2020 students would have gotten at the exams. The model-building was outsourced to a subcontractor undisclosed at the time of publishing this article.

A crisis erupted when the results came out in early July 2020. Tens of thousands of students all over the world received grades that not only deviated substantially from their predicted grades but did so in unexplainable ways. Some 24,000, or more than 15% of all 2020 IB diploma recipients, have since signed the protest.IBOs social media pages are flooded with furious comments.Several governments have also launched formal investigations, and numerous lawsuits are in preparation, some for data abuse under EUs GDPR. Whats more, schools, students, and families involved in other high school programs that have also adopted AI solutions are raising very similar concerns, notably in the UK, where A level results are due out on August 13th, 2020.

As the outrage has spread, one critical and very practical question has been consistently raised by frustrated students and parents: How can they appeal the grades?

In normal years, the appeals process was well-defined and consisted of several levels, from the re-marking of an individual students exam to a review of marks for course work by subject at a given school. The former means having another look at a students work a natural first step when the grades were based on such work. The latter refers to an adjustment that IBO may apply to a schools grading of course work should a sample of work independently assessed by the IBO produce substantially different grades, on average, from those awarded by the school. The appeal process was well-understood and produced consistent results, but was not used frequently, largely because, as noted, there were few surprises when the final grades came out.

This year, the IB schools initially treated appeals as requests for re-marks of student work. But this poses a fundamental challenge: the graded papers were not in dispute it was the AI assessment that was called into question. The AI did not actually correct any papers; it only produced final grades based on the data it was fed, which included teacher-corrected coursework and the predicted grades. Since the specifics of the program are not disclosed, all people can see are the results, many of which were highly anomalous, with final scores in some cases well below the marks of the teacher-graded coursework of the students involved. Unsurprisingly, the IBOs appeals approach has not met with success it is in no way aligned with the way in which the AI created the grades.

The main lesson coming out of this experience is that any organization that decides to use an AI to produce an outcome as critical and sensitive as a high-school grade marking 12-years of students work, needs to be very clear about how the outcomes are produced and how they can be appealed in the event that they appear anomalous or unexpected. From the outside, it looks as though the IBO may have simply plugged the AI into the IB system to replace the exams and then assumed that the rest of the system in particular the appeals process could work as before.

So what sort of appeals process should the IBO have designed? First of all, the overall process of scoring and, more important, appealing the decision should be easy to explain, so that people understand what each next step will be. Note that this is not about explaining the AI black box, as current regulators do when arguing about the need for explainable AI. That would be almost impossible in many cases, since understanding the programming used in an AI generally requires a high level of technical sophistication. Rather, it is about making sure that people understand what information is used in assessing grades and what the steps are in the appeal process itself. So what the IBO could have done instead was offer appellants the right to a human-led re-evaluation of anomalous grades, specify what input data the appeal committee would focus on in reanalyzing the case, and explain how the problem would be fixed.

How the problem would be fixed would depend on whether the problem turned out to be student specific, school specific, or subject specific; a single students appeal might well affect other students depending on what components of the AI the appeal may relate to.

If, for example, a problem with an individual students grade seems to be driven by the school level data possibly a number of students studying in that same school have had final grades that differed markedly from their predicted grades then the appeal process would look at the grades of all students in that school. If needed, the AI algorithm itself would be adjusted for the school in question, without however affecting other schools, making sure the new scores provided by the AI are consistent across all schools while remaining the same for all but one school. In contrast, if the problem is linked to factors specific to the student, then the analysis would focus on identifying why the AI produced an anomalous outcome for that student and, if needed, re-score that student and any other student whose grades were affected in the same way.

Of course, much of this would be true of any grading process one students anomaly might signal a more systematic failing in any grading process whether or not an AI is engaged. But the way in which the appeal process is designed needs to reflect the different ways in which humans and machines make decisions and the specific design of the AI used as well as how the decisions can be corrected.

For example, because AI awards grades on the basis of its model of relationships between various input data, there should generally be no need to look at the actual work of the students concerned, and corrections could be made to all affected students (those with similar input data characteristics) all at once. In fact, in many ways appealing an AI grade could be an easier process than appealing a traditional exam-based grade.

Whats more, with an AI system, an appeals process along the lines described would enable continuous improvement to the AI. Had the IBO put such a system in place, the results of the appeals would have produced feedback data that could have updated the model for future uses in the event, say, that examinations are again cancelled next year.


The IBOs experience obviously has lessons for deploying AI in many contexts from approving credit, to job search or policing. Decisions in all these cases can, as with the IB, have life altering consequences for the people involved. It is inevitable that disputes over the outcomes will occur, given the stakes involved. Including AI in the decision-making process without carefully thinking through an appeals process and linking the appeals process to the algorithm design itself will likely end not only with new crises but potentially with a rejection of AI-enabled solutions in general. And that deprives us all of the potential for AI, when combined with humans, to dramatically improve the quality of decision-making.

Disclosure: One of the authors of this article is the parent of a student completing the IB program this year.

See more here:

What Happens When AI is Used to Set Grades? - Harvard Business Review

Posted in Ai

Spotify Offers Personalized Artificial Intelligence Experience With The Weeknd – HYPEBEAST

With the help of new artificial intelligence technology, Spotify is providing fans with a highly-personalized way to experience The Weeknds critically acclaimed After Hours album. The microsite experience features a life-like version of The Weeknd, who will have a one-on-one chat with fans.

Upon entering the site, The Weeknds alter ego will appear on the screen. Hell start out by addressing each fan by name, and, based on listening data, share how theyve streamed his music over the years. The AI experience then turns into an intimate listening session of After Hours, one that is just between the individual and The Weeknd. Spotifys new Alone With Me experience comes after the streaming platform gave fans an exclusive remote listening party and Q&A to celebrate the release of The Weeknds new album back in March. The Alone With Me session gives the title of the album a whole new meaning.

Join The Weeknd in the Alone With Me experience on Spotifys website now.

In other music-related news, check out former U.S. President Barack Obamas annual Summer playlist.


Spotify Offers Personalized Artificial Intelligence Experience With The Weeknd - HYPEBEAST

Posted in Ai

A college kids fake, AI-generated blog fooled tens of thousands. This is how he made it. – MIT Technology Review

GPT-3 is OpenAIs latest and largest language AI model, which the San Franciscobased research lab began drip-feeding out in mid-July. In February of last year, OpenAI made headlines with GPT-2, an earlier version of the algorithm, which it announced it would withhold for fear it would be abused. The decision immediately sparked a backlash, as researchers accused the lab of pulling a stunt. By November, the lab had reversed position and released the model, saying it had detected no strong evidence of misuse so far.

The lab took a different approach with GPT-3; it neither withheld it nor granted public access. Instead, it gave the algorithm to select researchers who applied for a private beta, with the goal of gathering their feedback and commercializing the technology by the end of the year.

Porr submitted an application. He filled out a form with a simple questionnaire about his intended use. But he also didnt wait around. After reaching out to several members of the Berkeley AI community, he quickly found a PhD student who already had access. Once the graduate student agreed to collaborate, Porr wrote a small script for him to run. It gave GPT-3 the headline and introduction for a blog post and had it spit out several completed versions. Porrs first post (the one that charted on Hacker News), and every post after, was copy-and-pasted from one of the outputs with little to no editing.

From the time that I thought of the idea and got in contact with the PhD student to me actually creating the blog and the first blog going viralit took maybe a couple of hours, he says.


The trick to generating content without the need for much editing was understanding GPT-3s strengths and weaknesses. It's quite good at making pretty language, and it's not very good at being logical and rational, says Porr. So he picked a popular blog category that doesnt require rigorous logic: productivity and self-help.

From there, he wrote his headlines following a simple formula: hed scroll around on Medium and Hacker News to see what was performing in those categories and put together something relatively similar. Feeling unproductive? Maybe you should stop overthinking, he wrote for one. Boldness and creativity trumps intelligence, he wrote for another. On a few occasions, the headlines didnt work out. But as long as he stayed on the right topics, the process was easy.

After two weeks of nearly daily posts, he retired the project with one final, cryptic, self-written message. Titled What I would do with GPT-3 if I had no ethics, it described his process as a hypothetical. The same day, he also posted a more straightforward confession on his real blog.


Porr says he wanted to prove that GPT-3 could be passed off as a human writer. Indeed, despite the algorithms somewhat weird writing pattern and occasional errors, only three or four of the dozens of people who commented on his top post on Hacker News raised suspicions that it might have been generated by an algorithm. All those comments were immediately downvoted by other community members.

For experts, this has long been the worry raised by such language-generating algorithms. Ever since OpenAI first announced GPT-2, people have speculated that it was vulnerable to abuse. In its own blog post, the lab focused on the AI tools potential to be weaponized as a mass producer of misinformation. Others have wondered whether it could be used to churn out spam posts full of relevant keywords to game Google.

Porr says his experiment also shows a more mundane but still troubling alternative: people could use the tool to generate a lot of clickbait content. It's possible that there's gonna just be a flood of mediocre blog content because now the barrier to entry is so easy, he says. I think the value of online content is going to be reduced a lot.

Porr plans to do more experiments with GPT-3. But hes still waiting to get access from OpenAI. Its possible that theyre upset that I did this, he says. I mean, its a little silly.

Update: Additional details have been added to the text and photo captions to explain how Liam Porr created his blog and got it to the top of Hacker News.


A college kids fake, AI-generated blog fooled tens of thousands. This is how he made it. - MIT Technology Review

Posted in Ai

Artificial Intelligence of Things: AIoT Market by Technology and Solutions 2020 – 2025 – PRNewswire

NEW YORK, Aug. 18, 2020 /PRNewswire/ --

Overview:This AIoT market report provides analysis of technologies, leading companies and solutions. The report also provides quantitative analysis including market sizing and forecasts for AIoT infrastructure, services, and specific solutions for the period 2020 through 2025. The report also provides an assessment of the impact of 5G upon AIoT (and vice versa) as well as blockchain and specific solutions such as Data as a Service, Decisions as a Service, and the market for AIoT in smart cities.

Read the full report: https://www.reportlinker.com/p05951233/?utm_source=PRN

While it is no secret that AI is rapidly becoming integrated into many aspects of ICT, many do not understand the full extent of how it will transform communications, applications, content, and commerce. For example, the use of AI for decision making in IoT and data analytics will be crucial for efficient and effective smart city solutions in terms of decision making.

The convergence of AI and Internet of Things (IoT) technologies and solutions (AIoT) is leading to "thinking" networks and systems that are becoming increasingly more capable of solving a wide range of problems across a diverse number of industry verticals. AI adds value to IoT through machine learning and improved decision making. IoT adds value to AI through connectivity, signaling, and data exchange.

AIoT is just beginning to become part of the ICT lexicon as the possibilities for the former adding value to the latter are only limited by the imagination. With AIoT, AI is embedded into infrastructure components, such as programs, chipsets and edge computing, all interconnected with IoT networks. APIs are then used to extend interoperability between components at the device level, software level and platform level. These units will focus primarily on optimizing system and network operations as well as extracting value from data.

While early AIoT solutions are rather monolithic, it is anticipated that AIoT integration within businesses and industries will ultimately lead to more sophisticated and valuable inter-business and cross-industry solutions. These solutions will focus primarily upon optimizing system and network operations as well as extracting value from industry data through dramatically improved analytics and decision-making processes. Six key areas that the analyst sees within the scope of AIoT solutions are: Data Services Asset Management Immersive Applications Process Improvement Next Gen UI and UX Industrial Automation

Many industry verticals will be transformed through AI integration with enterprise, industrial, and consumer product and service ecosystems. It is destined to become an integral component of business operations including supply chains, sales and marketing processes, product and service delivery and support models.

From the perspective of the analyst, we see AIoT evolving to become more commonplace as a standard feature from big analytics companies in terms of digital transformation for the connected enterprise. This will be realized in infrastructure, software, and SaaS managed service offerings. More specifically, we see 2020 as a key year for IoT data-as-a-service offerings to become AI-enabled decisions-as-a-service-solutions, customized on a per industry and company basis. Certain data-driven verticals such as the utility and energy services industries will lead the way.

As IoT networks proliferate throughout every major industry vertical, there will be an increasingly large amount of unstructured machine data. The growing amount of human-oriented and machine generated data will drive substantial opportunities for AI support of unstructured data analytics solutions. Data generated from IoT supported systems will become extremely valuable, both for internal corporate needs as well as for many customer-facing functions such as product life-cycle management.

The use of AI for decision making in IoT and data analytics will be crucial for efficient and effective decision making, especially in the area of streaming data and real-time analytics associated with edge computing networks. Real-time data will be a key value proposition for all use cases, segments, and solutions. The ability to capture streaming data, determine valuable attributes, and make decisions in real-time will add an entirely new dimension to service logic.

In many cases, the data itself, and actionable information will be the service. AIoT infrastructure and services will, therefore, be leveraged to achieve more efficient IoT operations, improve human-machine interactions and enhance data management and analytics, creating a foundation for IoT Data as a Service (IoTDaaS) and AI-based Decisions as a Service.

The fastest-growing 5G AIoT applications involve private networks. Accordingly, the 5GNR market for private wireless in industrial automation will reach $4B by 2025. Some of the largest market opportunities will be AIoT market IoTDaaS solutions. The analyst sees machine learning in edge computing as the key to realizing the full potential of IoT analytics.

Target Audience: AI companies IoT companies Robotics companies Semiconductor vendors Data management vendors Industrial automation companies Governments and R&D organizations

Select Report Findings: The global AIoT market will reach $65.9B by 2025, growing at 39.1% CAGR The global market for IoT data as service solutions will reach $8.2B USD by 2025 The AI enabled edge device market will be the fastest growing segment within the AIoT AIoT automates data processing systems, converting raw IoT data into useful information Today's AIoT solutions are the precursor to next generation AI Decision as a Service (AIDaaS)

Companies in Report: AB Electrolux ABB Ltd. AIBrian Inc. Alibaba Alluvium Amazon Inc. Analog Devices Apple Inc. ARM Limited Arundo Analytics Atmel Corporation Ayla Networks Inc. Baidu Brighterion Inc. Buddy C3 IoT Canvass Analytics Cisco CloudMinds Cumulocity GmBH Cypress Semiconductor Corp Digital Reasoning Systems Inc. DT42 Echelon Corporation Enea AB Express Logic Inc. Facebook Inc. Falkonry Fujitsu Ltd. Gemalto N.V. General Electric General Vision Inc. Google Gopher Protocol Graphcore H2O.ai Haier Group Corporation Helium Systems Hewlett Packard Enterprise Huawei Technologies IBM Corp. Infineon Technologies AG Innodisk Intel Corporation Interactor Juniper Networks Losant IoT Micron Technology Microsoft Corp. Nokia Corporation Nvidia Oracle Corporation Pepper PTC Corporation Qualcomm Robert Bosch GmbH Salesforce Inc. SAS Sharp ShiftPixy Siemens AG SK Telecom SoftBank Robotics SpaceX SparkCognition STMicroelectronics Symantec Corporation Tellmeplus Tencent Tend.ai Terminus Tesla Texas Instruments Thethings.io Tuya Smart Uptake Veros Systems Whirlpool Corporation Wind River Systems Xiaomi Technology

Read the full report: https://www.reportlinker.com/p05951233/?utm_source=PRN

About Reportlinker ReportLinker is an award-winning market research solution. Reportlinker finds and organizes the latest industry data so you get all the market research you need - instantly, in one place.

Contact Clare: [emailprotected] US: (339)-368-6001 Intl: +1 339-368-6001

SOURCE Reportlinker


Original post:

Artificial Intelligence of Things: AIoT Market by Technology and Solutions 2020 - 2025 - PRNewswire

Posted in Ai

‘T-Minus AI’: A look at the intersection of geopolitics and autonomy – C4ISRNet

China has a national plan for it. Russia says it will determine the ruler of the world. The United States is investing heavily to develop it.

The race is on to create, control and weaponize artificial intelligence.

In Michael Kanaans book T-Minus AI: Humanitys Countdown to Artificial Intelligence and the New Pursuit of Global Power, set for release Aug. 25, the realities of AI from a human-oriented perspective are laid out for the reader. Such technology, often shrouded in mystery and misunderstood, is made easy to comprehend through a discussion on the global implications of developing AI. Kanaan is one of the Air Forces AI leaders.

The following excerpt, edited for length and clarity, introduces how, in late 2017, the conversation about artificial intelligence changed forever.

It was a Friday morning, Sept. 1, 2017, and not yet dawn when I stepped out of Reagan National Airport and followed my bag into the back of a waiting SUV. After flying east all night from San Francisco to D.C., I still had two hours before a Pentagon briefing with Lt. Gen. VeraLinn Dash Jamieson. She was the deputy chief of staff for U.S. Air Force intelligence and the countrys most senior Air Force intelligence officer, a three-star officer responsible for a staff of 30,000 and an overall budget of $55 billion.

As the Air Force lead officer for artificial intelligence and machine learning, Id been reporting directly to Jamieson for over two years. The briefing that morning was to discuss the commitments wed just received from two of Silicon Valleys most prominent AI companies. After months of collective effort, the new agreements were significant steps forward. They were also crucial proof that the long history of cooperation between the American public and private sectors could reasonably be expected to continue. With the world marching steadfastly into the promising but unsettled fields of AI, it was becoming critical that Americans do so, if not entirely in harmony, then at least to the sounds of the same beat.

My apartment was only a short ride away. I was looking forward to a hot shower and strong coffee. But as the SUV pulled out of the terminal and into the morning darkness, a message alert pinged from my phone. It was a text from the general. Short and to the point, as usual. See Putin comments re AI.

A quick web search pulled up a quote already posting to news feeds everywhere. At a televised symposium broadcast throughout Russia only an hour earlier, President Vladimir Putin had crafted a sound bite making headlines around the globe. His unambiguous three sentences translated to: Artificial intelligence is the future, not only for Russia, but for all humankind. It comes with colossal opportunities, but also threats that are difficult to predict. Whoever becomes the leader in this sphere will become the ruler of the world.

Sign up for the C4ISRNET newsletter about future battlefield technologies.

(please select a country) United States United Kingdom Afghanistan Albania Algeria American Samoa Andorra Angola Anguilla Antarctica Antigua and Barbuda Argentina Armenia Aruba Australia Austria Azerbaijan Bahamas Bahrain Bangladesh Barbados Belarus Belgium Belize Benin Bermuda Bhutan Bolivia Bosnia and Herzegovina Botswana Bouvet Island Brazil British Indian Ocean Territory Brunei Darussalam Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Cape Verde Cayman Islands Central African Republic Chad Chile China Christmas Island Cocos (Keeling) Islands Colombia Comoros Congo Congo, The Democratic Republic of The Cook Islands Costa Rica Cote D'ivoire Croatia Cuba Cyprus Czech Republic Denmark Djibouti Dominica Dominican Republic Ecuador Egypt El Salvador Equatorial Guinea Eritrea Estonia Ethiopia Falkland Islands (Malvinas) Faroe Islands Fiji Finland France French Guiana French Polynesia French Southern Territories Gabon Gambia Georgia Germany Ghana Gibraltar Greece Greenland Grenada Guadeloupe Guam Guatemala Guinea Guinea-bissau Guyana Haiti Heard Island and Mcdonald Islands Holy See (Vatican City State) Honduras Hong Kong Hungary Iceland India Indonesia Iran, Islamic Republic of Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Kiribati Korea, Democratic People's Republic of Korea, Republic of Kuwait Kyrgyzstan Lao People's Democratic Republic Latvia Lebanon Lesotho Liberia Libyan Arab Jamahiriya Liechtenstein Lithuania Luxembourg Macao Macedonia, The Former Yugoslav Republic of Madagascar Malawi Malaysia Maldives Mali Malta Marshall Islands Martinique Mauritania Mauritius Mayotte Mexico Micronesia, Federated States of Moldova, Republic of Monaco Mongolia Montserrat Morocco Mozambique Myanmar Namibia Nauru Nepal Netherlands Netherlands Antilles New Caledonia New Zealand Nicaragua Niger Nigeria Niue Norfolk Island Northern Mariana Islands Norway Oman Pakistan Palau Palestinian Territory, Occupied Panama Papua New Guinea Paraguay Peru Philippines Pitcairn Poland Portugal Puerto Rico Qatar Reunion Romania Russian Federation Rwanda Saint Helena Saint Kitts and Nevis Saint Lucia Saint Pierre and Miquelon Saint Vincent and The Grenadines Samoa San Marino Sao Tome and Principe Saudi Arabia Senegal Serbia and Montenegro Seychelles Sierra Leone Singapore Slovakia Slovenia Solomon Islands Somalia South Africa South Georgia and The South Sandwich Islands Spain Sri Lanka Sudan Suriname Svalbard and Jan Mayen Swaziland Sweden Switzerland Syrian Arab Republic Taiwan, Province of China Tajikistan Tanzania, United Republic of Thailand Timor-leste Togo Tokelau Tonga Trinidad and Tobago Tunisia Turkey Turkmenistan Turks and Caicos Islands Tuvalu Uganda Ukraine United Arab Emirates United Kingdom United States United States Minor Outlying Islands Uruguay Uzbekistan Vanuatu Venezuela Viet Nam Virgin Islands, British Virgin Islands, U.S. Wallis and Futuna Western Sahara Yemen Zambia Zimbabwe


By giving us your email, you are opting in to the C4ISRNET Daily Brief.

As the driver accelerated up the Interstate 395 ramp toward the city, a heavy rain started to fall, hitting hard against the cars metal surfaces. Far off, through the window on my right, the dome of the Capitol building glistened in white light beyond the blurred, dark space of the Potomac River. Playing at background volume over the front speakers, a National Public Radio newscaster was describing a 3-mile-wide asteroid named Florence. Streaking past our planet that morning, the massive rock would be little more than 4 million miles away at its closest point tremendously far by human standards, but breathtakingly near by the infinite scales of space. It was the largest object NASA had ever tracked to pass so closely by our planet. On only a slightly different trajectory, it would have altered Earths entire landscape. And, like for the dinosaurs before us, it would have changed everything. It would have changed life. A perfect metaphor, I thought, impeccably timed to coincide with Putins comments about AI.

I looked back at his words. The message they carried rang like an alarm I didnt need to hear, but the motivation behind them wasnt so clear. Former KGB officers speak carefully and only for calculated reasons. Putin is no exception. His words matter, always. And so does his purpose. But what was it here? Just to offer a commentary or forecast? No. Not his style. A call to action, then, to energize his own population? Perhaps. But, more than that, this was a statement to other statesmen, a confirmation that he and his government were awake and aware that a sophisticatedly deep effort was underway to accomplish a new world order.

Only a month earlier, China had released a massive three-part strategy aimed at achieving very clear benchmarks of advances in AI. First, by 2020, China planned to match the highest levels of AI technology and application capabilities in the U.S. or anywhere else in the world. Second, by 2025, they intend to capture a verifiable lead over all countries in the development and production of core AI technologies, including voice- and visual-recognition systems. Last, by 2030, China intends to dominantly lead all countries in all aspects and related fields of AI. To be the sole leader, the worlds unquestioned and controlling epicenter of AI. Period. That is Chinas declared national plan.

With the Chinese governments newly published AI agenda available for the world to see, Putins words resolved any ambiguity about its implication. True to his style, his message was clear and concise. Whoever becomes the leader will become the ruler of the world.

Straightforward, I thought. And hes right. But focused administrations around the globe already know the profound potential of AI. The Chinese clearly do its driving their domestic and foreign agendas. And the Saudis, the European Union nations, the U.K., and the Canadians they know it, too. And private enterprise is certainly focused in, from Google, Facebook, Amazon, Apple and Microsoft to their Chinese state-controlled counterparts Baidu, Alibaba, Tencent and the telecom giant Huawei.

AI technologies have been methodically evolving since the 1960s, but over most of those years, the advances were sporadic and relatively slow. From the earliest days, private funding and government support for AI research ebbed and flowed in direct relation to the successes and failures of the latest predictions and promises. At the lowest points of progress, when little was being accomplished, investment capital dried up. And when it did, efforts slowed. It was the usual interdependent circle of cause and effect. Twice, during the late 70s and then again during the late 80s and early 90s, the pace of progress all but stopped. Those years became known as the AI winters.

But, in the last 10 to 15 years, a number of major breakthroughs, in machine learning in particular, again propelled AI out of the dark and into another invigorated stage. A new momentum emerged, and an unmistakable race started to take shape. Insightful governments and industry leaders began doing everything possible to stay within reach of the lead, positioning themselves for any possible path to the front.

Now, for all to hear, Putin had just declared everything at stake. Without any room for misunderstanding, he equated AI superiority to global supremacy, to a strength akin to economic or even nuclear domination. He said it for public consumption, but it was rife with political purpose. Whoever becomes the leader in this sphere will become the ruler of the world.

Those words would undoubtedly add another level of urgency to the days meetings. That was certain. I redirected the driver to the Pentagon and looked down at my phone to answer the generals text. Landed. Saw quote. On my way in.

The shower would have to wait.


In the months that followed, Putins now infamous few sentences proved impactful across continents, industries and governments. His comments provided the additional, final push that accelerated the planets sense of seriousness about AI and propelled most everyone into a higher gear forward. Public and private enterprises around the globe reassessed their focuses and levels of commitment. Governments and industries that had previously dedicated only minimal percentages of their research and defense budgets to the new technology suddenly saw things differently. It quickly became unacceptable to slow-walk AI efforts and protocols, and no longer defensible to incubate AI innovations for longer than the shortest time necessary.

Now, not long after, the pace of the race has quickened to a full sprint. National strategies and demonstratable use have become the measurements that matter. Rollouts have become requisite. To accomplish them, agendas are more focused, aggressive and well funded. Sooner than many expected, AI is proving itself a dominant force of economic, political and cultural influence, and is poised to transform much of what we know and much of what we do. China, Russia and others are utilizing AI in ways the world needs to recognize. Thats not to say all efforts and iterations in the West are without criticism. Theyre not. But if this new technology causes or contributes to a shift in power from the West to the East, everyone will be affected. Everything will change.

The future is here, and the world ahead looks far different than ever before.

No longer just science fiction or fantastic speculation, artificial intelligence is real. Its here, all around us, and it has already become an integral and influential part of our lives. Although weve taken only our first few steps into this new frontier of technological innovation, AI is providing us powerful new methods of conducting our affairs and accomplishing our goals. We use these new tools every day, usually without choice and often without even realizing it from applications that streamline our personal lives and social activities to business programs and practices that enable new ways of acquiring a competitive advantage. Ive learned a lot about the common misperceptions and misgivings people have when trying to understand AI. Most conversations about artificial intelligence either begin or end with one or more of the following questions:

Although the answers to those questions merit long discussions and are open to differing opinions, they should at least be manageable and factually accurate. The topics shouldnt be too difficult to discuss or debate not conversationally or even at policymaking or political levels. Unfortunately, they generally are.

But the conversational disconnects that usually occur arent because of some complex technical details or confusing computer issues. Instead, its usually, simply, because of the same old obstacles that too often stand in the way of many other conversations. Regardless of the topic, and even when it matters most, we too frequently speak below, above, around or past one another especially when we dont have an equal amount of information, a shared base of knowledge or a common set of experiences. In those instances, we make too many assumptions, allow too many things to go without saying and use too many words that hold different meanings for different people. In short, too many confusions are never clarified and too many more are created. As a consequence, were doomed for frustration and failure from the start, inevitably unable to understand one another and incapable of appreciating each others perspectives and talking points. My goal throughout this book is to avoid those pitfalls.

The best way to start is to first address the most common misperceptions of all, the ones we tend to bring with us into the AI conversation. The first of these is the assumption that AI is unavoidably destined, sooner or later, to develop its own consciousness and its own autonomous, evil intent. For that idea, we can thank science fiction and the entertainment industry. Make no mistake, Im an ardent fan of science fiction, both on screen and in books. Without any doubt, the sci-fi genre has given us fine works of imagination, insight and art. Many great fiction writers and filmmakers are extremely knowledgeable about technology and conscientiously concerned about our future. Time and again theyve proven themselves true visionaries, and were unquestionably better off for their work. They spark our curiosity, ignite our imaginations, increase our appetite for knowledge, and encourage our interests in science and societal issues.

But when it comes to their scientific portrayals of artificial intelligence, our most popular authors and screenwriters have too often generated an array of exotic fears by focusing our attention on distant, dystopian possibilities instead of present-day realities. Science fiction that depicts AI usually aligns a computers intelligence with consciousness, and then frightens us by portraying future worlds in which AI isnt only conscious, but also evil-minded and intent, self-motivated even, to overtake and destroy us. To create drama, there has to be conflict, and the humans in these stories are almost always overwhelmed and outmatched, naturally unable to compete against the machines vastly superior intelligence and mechanical strength. Iconic movies like 2001: A Space Odyssey, The Matrix, The Terminator, Ex Machina, and I, Robot, along with television series such as Westworld and Black Mirror, have turned our underlying fears and suspicions into deep-seated and bleak expectations.

Even today, commercial companies that offer AI products and consumer services routinely have to fight our distrust of intelligent machines as a basic, necessary part of their regular marketing efforts. Just think of all the television commercials for AI-enabled products we now see, and consider how many of them are focused first on trying to put us at ease by casting a polite and gentle glow to the figurative, artificial face of their AI, even when that face has absolutely nothing to do with the services their products actually provide.

AI is an extremely powerful tool, and it has immense implications we must consider and evaluate carefully. Its a very sharp instrument that shouldnt be callously wielded or casually accepted, especially when its in the wrong hands or when its used for intentionally intrusive or oppressive purposes. These are serious issues, and there are significant steps we must take to ensure AI is properly designed and implemented. Fortunately, and contrary to what many people think, its not necessary to have a background in computer science, mathematics or engineering in order to very meaningfully understand AI and its technological implications. With just a basic comprehension of a few fundamental concepts behind todays computers and related sciences, its entirely possible to connect the relevant dots and understand the overall picture.

Creating tools to facilitate our lives is the strength of humankind. Its what we do. Given enough time, it was arguable, perhaps even inevitable, that we would create the ultimate tool artificial intelligence itself. But what exactly does it mean that weve accomplished that task? And how is AI even possible? In large part, the answers lie in the history of ourselves and of our own biological intelligence. It turns out that artificially replicating what we know about the human thought process, at least as best we can, is a highly effective blueprint for creating something similar in a machine. Its our own evolution and our own history that teach us the fundamentals that make it all possible.

Read the rest here:

'T-Minus AI': A look at the intersection of geopolitics and autonomy - C4ISRNet

Posted in Ai

Exclusive: Eshoo On AI, Cybersecurity And Kicking America Off The China Drug Habit – Forbes

WASHINGTON, DC - Chairman Rep. Anna Eshoo (D-Calif.) is seen during a House Energy and Commerce ... [+] Subcommittee on Health hearing to discuss protecting scientific integrity in response to the coronavirus outbreak on Thursday, May 14, 2020. in Washington, DC. (Photo by Greg Nash-Pool/Getty Images)

Congresswoman Anna Eshoo (CA-18) was first elected to Congress in 1992. She has served on the Energy and Commerce Committee since 1995 with a focus on health and technology. Last year she became the first woman ever to serve as Chair of the Health Subcommittee. She has authored 41 bills signed into law by four presidents. I was able to speak to Congresswoman Eshoo about her recent accomplishment and her agenda for AI, cybersecurity, and medical supply chains.

RL: Representative Eshoo, thank you for your bipartisan leadership to create a national strategy to end dependence of foreign manufacturing of lifesaving drugs. What is the status of this bill? What can your experience from pharmaceuticals supply chain security teach us about other critical areas for supply chain security, such as information technology?

Ive championed the need to address our nations overreliance on the foreign production of critical drugs in Congress. Last September, I co-authored a Washington Post Op-Ed about our dangerous and troubling reliance on China for the manufacturing of drugs and their ingredients. Soon after, I held a hearing in my Health Subcommittee about the consequences and complications of our global drug supply chain. On May 1st I introduced bipartisan legislation, the Prescription for American Drug Independence Act, which requires the National Academies of Sciences, Engineering, and Medicine to convene a committee of experts to analyze the impact of U.S. dependence on the manufacturing of lifesaving drugs and make recommendations to Congress within 90 days to ensure the U.S. has a diverse drug supply chain to adequately protect our country from natural or hostile occurrences. The legislation was included in the House-passed Heroes Act and I look forward to the Senate taking it up.

You are correct to note that an overreliance on China is not unique to the drug supply chain. For a decade Ive raised how the vulnerabilities in our telecommunications infrastructure directly impact our national security. On November 2, 2010, I wrote to the FCC expressing grave concerns about Huawei and ZTE, which have opaque entanglements with the Chinese government. Sadly, in the intervening decade Huawei and ZTE equipment has proliferated across our country because its cheap, due to the Chinese government subsidizing them. Weve passed several important measures this Congress that Im proud to support, including measures to create a mechanism for the federal government to exclude Huawei and ZTE equipment from our networks and to establish a program to rip and replace existing equipment made by the companies.

RL: It was great to see bipartisan and bicameral support for the National AI Research Resource Task Force Act under your leadership These are much-needed policies measures. What else do we need to do on this front? What is are your objectives in this area for the next Congress?

Im very proud of the smart tech-related provisions in the House-passed H.R. 6395, the William M. (Mac) Thornberry National Defense Authorization Act for Fiscal Year 2021, or the NDAA.

The Global Al Index indicates that the U.S. is ahead of China in the global AI race today but experts predict China will overtake the U.S. in just five to 10 years. Im pleased that the NDAA includes several important AI efforts, including my bipartisan and bicameral legislation, H.R. 7096, the National AI Research Resource Task Force Act, which establishes a task force to develop a roadmap for a national AI research cloud to make available high-powered computing, large data sets, and educational resources necessary for AI research.

You ask what else is needed in addition to these provisions. In AI, the answer is federal R&D funding. Earlier this year, I wrote to the House Appropriations Committee urging them to allocate robust funding for nondefense AI R&D, and seventeen of my House colleagues joined my letter. This funding is an important investment in our countrys future and must be a priority.

On cybersecurity, Im pleased the NDAA included a number of recommendations from the Cyberspace Solarium Commission which Congress established in last years NDAA. Cybersecurity must be a top priority for every company and for government. It is a domain that works best when companies, researchers, and government work hand-in-hand. Unfortunately, cybersecurity efforts operate in silos across the private sector and within government. We need coordination. Its for this reason I cosponsored legislation to establish a centralized cybersecurity coordinator the National Cyber Director in the White House.

A gap I see is the cybersecurity of what I call small organizations small businesses, nonprofits and local governments that are too small to ever employ a cybersecurity professional and may never have the budget to pay for security services. While 50-page technical and legalistic government documents are critical for cybersecurity teams within large organizations, they are too dense for small business owners, executive directors of nonprofits, and city managers of small municipalities. Im currently drafting legislation to address this issue that should be ready to introduce shortly.

I was also pleased the House adopted an amendment I cosponsored that is based on the CHIPS for America Act, which will restore American leadership in semiconductor manufacturing. In the House, I represent much of Silicon Valley, a region that gets its name from the material used to make semiconductors. While the technology sector has evolved to include much more than semiconductor manufacturing, it remains the foundation of one of the most vibrant parts of our economy. Our militarys dependence on semiconductor manufacturing is why its a national security priority, and Im hopeful that the CHIPS for America Act will be enacted into law as soon as possible.

RL: In my own research, I have uncovered that California state government itself has set up purchasing agreements with Chinese-government owned firms like Lenovo, Lexmark, and others. As you well know, the Chinese government asserts its right to collect any data on any Chinese-made device anywhere for any reason. China has been building a database on Americans since 2015. Having Chinese owned equipment in state government is a risk particularly around elections. In any event, it appears that these contracts have been set up by procurement officers who are not aware of the security risks.I attribute this to the lack of communication between the federal government and the states themselves. How could Congress engage constructively with states to help them improve their privacy and security practices in this regard?

You raise a number of highly important points. When it comes to evolving technologies, thinking about privacy and security is critical at every step of policymaking and at every level of government. Laws and regulations need to require privacy and security. Vendor selection should always consider privacy and cybersecurity, especially when issues intersect with national security. And governmental oversight needs to review privacy and security issues.

The federal government must share threat and vulnerability information more reliably. We cant expect every procurement manager in every municipal government to be aware of the national security concerns related to routers, modems, printers, and myriad other internet-connected devices and electronics. National security is the domain of the federal government. In addition to protecting individual Americans, the federal governments responsibility includes protecting our governmental (federal, state, and local) and our economic interests.

RL: Thank you, Congresswoman Eshoo.

See original here:

Exclusive: Eshoo On AI, Cybersecurity And Kicking America Off The China Drug Habit - Forbes

Posted in Ai

Hour One wants synthetic AI characters to be your digital avatars – VentureBeat

If you ever wondered how well populate the metaverse, look no further than Hour One, an Israeli startup that is making replicas of people with AI avatars. These avatars can be a near-perfect visual likeness of you and speak with words fed to them by marketers who want to sell you something. An avatar can speak on your behalf in a digital broadcast when youre at home watching TV.

Such creations feel like a necessary prerequisite of the metaverse, the universe of virtual worlds that are all interconnected, like in novels such asSnow CrashandReady Player One. And the trick: Youll never know if youre talking to a real person or one of Hour Ones synthetic people.

There is definitely interest in the metaverse and we are doing experiments in the gaming space and with photorealism, Hour One business strategy lead Natalie Monbiot said in an interview with VentureBeat. The thing that has fired up the team is this vision of a world which is increasingly virtual and a belief that we will live increasingly virtually.

She added, We already have different versions of ourselves that appear in social media and different social channels. We represent ourselves already in this kind of digital realm. And we believe that our virtual selves will become even more independent. And we can put them to work for us. We can benefit from this as a human race. And you know, that old saying we cant be in two places at once? Well, we believe that that will no longer be true.

Hour One is one more example of the fledgling market for virtual beings. Startups focused on virtual beings have raised more than $320 million to date, according to Edward Saatchi of Fable Studios, speaking at Julys Virtual Beings Summit.

But were a little ahead of ourselves. Metaverse plays are becoming increasingly common as we all realize that there has to be something better than Zoom calls to engage in a digital way. So the Tel Aviv, Israel-based company said it raised $5 million in seed funding this week from Galaxy Interactive via its Galaxy EOS VC Fund, as well as Block.one, Remagine Ventures, Kindred Ventures, and Amaranthine. It will use that money to scale its AI-driven cloud platform and create thousands of new digital characters.

Youve heard of stock photos. Hour One is talking about something similar to stock humans. They can be used to speak any kind of script in a marketing video or give a highly customized message to someone. The goal is to create characters who cross the uncanny valley.

I think that weve crossed the uncanny valley because we have our likeness test, and our videos are actually live and in market and generating results for customers, Monbiot said. I think thats something thats really distinctive about us, even though were such a young company, weve had very positive commercial traction already.

Above: Whos real and whos not?

Image Credit: Hour One

We create synthetic characters based on real people, Monbiot said. We do so for commercials. We take real people and we have this really simple process for converting real people into synthetic characters that resemble them exactly. And once we have the synthetic characters, we can program them to generate all kinds of new content at enormous speed and scale.

The competition in this space will be tough. GamesBeat will be having our own conference, tentatively scheduled for January 26 to January 27, 2020, on topics including the metaverse, and we expect it to be full of interesting companies.

A Samsung spinoff, Neo, caught a lot of attention for creating human AI avatars at CES 2020 in January, and then it promptly caught a lot of bad press for avatars that didnt look as real as expected. But Hour One also started coming out of stealth at the same time with a plan to expand business-to-business human communication. The company showcased its real or synthetic likeness test at CES 2020, challenging people to distinguish between real and synthetic characters generated by its AI.

Hour One is using deep learning and generative adversarial neural networks to make its video characters. The company says it can do this in a highly scalable and cost-effective way. Theyre supposed to look good, and the image on top of this story looks realistic.

But the cost of missing the mark is high. Hour One will have to beat Neo in the race across the uncanny valley. And Genies is coming from another direction, with cartoon-based avatars that represent digital versions of celebrities.

Above: Hour Ones real Natalie Monbiot

Image Credit: Hour One

Hour One is working with companies in the ecommerce, education, automotive, communication, and enterprise sectors, with expanded industry applications expected throughout 2020. The company has about 100 avatars today.

The pitch is that the lower cost per character use means that companies will be able to engage more with their customers on every level, from digital receptionists to friendly salespeople.

These customers can create thousands of videos simply by submitting text to these characters, Monbiot said. It appears as though real people are actually saying those words, but were using AI to make it happen. Were improving communication. Were obviously living in an ever-more virtual existence. And were enabling businesses of all kinds to engage in a more human way.

And if your avatar is speaking on behalf of you somewhere and its generating value, youll get paid for it, Monbiot said even if youre not there. We have a very bright view of the future. If your avatar speaks, you can get paid for that, Monbiot said.So were at the beginning of a new future. And for us, thats a future in which everybody will have a synthetic character. We will have virtual versions of ourselves.

Sam Englebardt, managing director of Galaxy Interactive (and a speaker on the subject of the metaverse at our GamesBeat Summit event), calls the approach an ethical one.Hour One is a business-to-business provider of the best synthetic video tech Ive seen to date, Englebardt said in an email to GamesBeat.

Oren Aharon and Lior Hakim created Hour One in 2019 with a mission of driving the economy of the digital workforce powered by synthetic characters of real-life people. They can use blockchain technology to verify the identity of a digital character and who owns it. If theyre altered or used for deep fakes, Hour One will be able to mark them as altered and notify people what has happened. The team has eight people.

Read the original here:

Hour One wants synthetic AI characters to be your digital avatars - VentureBeat

Posted in Ai

How AI can help payers navigate a coming wave of delayed and deferred care – FierceHealthcare

So far insurers have seen healthcare use plummet since the onset of the COVID-19 pandemic.

But experts are concerned about a wave of deferred care that could hit as patients start to return to patients and hospitals putting insurers on the hook for an unexpected surge of healthcare spending.

Artificial intelligence and machine learning could lend insurers a hand.

Against Coronavirus, Knowledge is Power

For organizations with a need for affordable and convenient COVID-19 antibody testing, Truvian's Easy Check COVID-19 IgM/IgG antibody test empowers onsite testing at scale, with accurate results at 10 minutes from a small sample of blood. Hear from industry experts Dr. Jerry Yeo, University of Chicago and Dr. Stephen Rawlings, University of California, San Diego on the state of COVID antibody testing and Easy Check through our on-demand webinar.

We are using the AI approaches to try to protect future cost bubbles, said Colt Courtright, chief data and analytics officer at Premera Blue Cross, during a session with Fierce AI Week on Wednesday.

WATCH THE ON-DEMAND PLAYBACK:What Payers Should Know About How AI Can Change Their Business

He noted that people are not going in and getting even routine cancer screenings.

If people have delay in diagnostics and delay in medical care how is that going to play out in the future when we think about those individuals and the need for clinical programs and the cost and how do we manage that? he said.

Insurers have started in some ways to incorporate AI and machine learning in several different facets such as claims management and customer service, but insurers are also starting to explore how AI can be used to predict healthcare costs and outcomes.

In some ways, the pandemic has accelerated the use of AI and digital technologies in general.

If we can predict, forecast and personalize care virtually, then why not do that, said Rajeev Ronanki, senior vice president and chief digital officer for Anthem, during the session.

The pandemic has led to a boom in virtual telemedicine as the Trump administration has increased flexibility for getting Medicare payments for telehealth and patients have been scared to go to hospitals and physician offices.

But Ronanki said that AI cant just help with predicting healthcare costs, but also on fixing supply chains wracked by the pandemic.

He noted that the manufacturing global supply chain is extremely optimized, especially with just-in-time ordering that doesnt require businesses to have a large amount of inventory.

But that method doesnt really work during a pandemic when there is a vast imbalance in supply and demand with personal protective equipment, said Ronanki.

When you connect all those dots, AI can then be used to configure supply and demand better in anticipation of issues like this, he said.

Read more:

How AI can help payers navigate a coming wave of delayed and deferred care - FierceHealthcare

Posted in Ai

How AI & Machine Learning is Infiltrating the Fintech Industry? – Customer Think

Credits: freepik

Fintech is a buzzword in the modern world, which essentially means financial technology. It uses technology to offer improved financial services and solutions.

How AI and machine learning are making ways across industries, including fintech? Its an important question in the business world globally.

The use of artificial intelligence (AI) and machine learning (ML) is evolving in the finance market, owing to their exceptional benefits like more efficient processes, better financial analysis and customer engagement.

According to the prediction of Autonomous Research, AI technologies will allow financial institutions to reduce their operational costs by 22%, by 2030.AI and ML are truly efficient tools in the financial sector. In this blog, we are going to discuss how they actually help fintech? What benefits do these technologies can bring to the industry?

The implementation of AI and ML in the financial landscape has been transforming the industry. As fintech is a developing market, it requires industry-specific solutions to meet its goals. AI tools and machine learning can offer something great here.

Are you eager to know the impact of AI and ML on fintech? These disruptive technologies are not only effective in improving the accuracy level but also speeds up the entire financial process by applying various proven methodologies.

AI-based financial solutions are focused on the crucial needs of the modern financial sector such as better customer experience, cost-effectiveness, real-time data integration, and enhanced security. Adoption of AI and allied its applications enables the industry to create a better, engaging financial environment for its customers.

Use of AI and ML has facilitated financial and banking operations. With the help of such smart developments, fintech companies are delivering tailored products and services as per the needs of the evolving market.

According to a study by research group Forrester, around 50% of financial services and insurance companies already use AI globally. And the number is expected to grow with newer technology advancements.

You will be thinking why AI and ML are becoming more important in fintech? In this section, we explain how these technologies are infiltrating the industry.

The need for better, safer, and customized solutions is rising with expectations of customers. Automation has helped the fintech industry to provide better customer service and experience.

Customer-facing systems such as AI interfaces and Chatbots can offer useful advice while reducing the cost of staffing. Moreover, AI can automate the back office process and make it seamless.

Automation can greatly help Fintech firms to save time as well as money. Using AI and ML, the industry has ample opportunities for reducing human errors and improving customer support.

Finance, insurance and banking firms can leverage AI tools to make better decisions. Here management decisions are data-driven, which creates a unique way for management.

Machine learning effectively analyzes the data and brings required outcomes that help officials to cut costs. Also, it empowers organizations to solve specific problems effectively.

Technologies are meant to deliver convenience and improved speed. But, along with these benefits, there is also an increase in online fraud. Keeping this in mind, Fintech companies and financial institutions are investing in AI and machine learning to defeat fraudulent transactions.

AI and machine learning solutions are strong enough to react in real-time and can analyze more data quickly. The organizations can efficiently find patterns and recognize fraudulent process using different models of machine learning. The fintech software development company can help build secured financial software and apps using these technologies.

With AI and ML, a huge amount of data can be analyzed and optimized for better applications. Hence fintech is the right industry where there is a great future of AI and machine learning innovations.

Owing to their potential benefits, automation and machine learning are increasingly used in the Fintech industry. In the case of smart wallets, they learn and monitor users behaviour and activities, so that appropriate information can be provided for their expenses.

Fintech firms are working with development and technology leaders to bring new concepts that are effective and personalized. Artificial intelligence, machine learning, and allied technologies are playing a vital role in financial organizations to improve skills, customer satisfaction, and reduce costs.

In the developing world, it is crucial for fintech companies to categorize clients by data analyzing, and allied patterns. AI tools show excellent capabilities in it to automate the process of profiling clients, based on their risk profile. This profiling work helps experts give product recommendations to customers in an appropriate and automated way.

Predictive analytics is another competitive advantage of using AI tools in the financial sector. It is helpful to improve sales, optimize resource use, and enhance operational efficiency.

With machine learning algorithms, businesses can effectively gather and analyze huge data sets to make faster and more accurate predictions of future trends in the financial market. Accordingly, they can offer specific solutions for customers.

As the market continues to demand easier and faster transactions, emerging technologies, such as artificial intelligence and machine learning, will remain crucial for the Fintech sector.

Innovations based on AI and ML are empowering the Fintech industry significantly. As a result, financial institutions are now offering better financial services to customers with excellence.

Leading financial and banking firms globally are using the convenient features of artificial intelligence to make business more stable and streamlined.

Read more from the original source:

How AI & Machine Learning is Infiltrating the Fintech Industry? - Customer Think

Posted in Ai

Artificial Intelligence Applications within Retail in 2020 – ReadWrite

Artificial intelligence and its applications have surely revolutionized the sectors pushing them forward in a new direction. Its application isnt limited to the start of product development but continues post-launch and customer interaction.

One of the sectors that are reaping the benefits of AI integration is the retail industry. However, there are still many questions that are being thrown out there. From what AI-technology or application has proven to be the most beneficial in retail to which innovations have the potential to change the retail game?

We need to keep in mind that artificial intelligence has not been perfected and is still in the stages of experimentation. Some results have proven to be positive and progressive, while others a complete failure.

Having said this, from 2013 to 2018, AI startups have raised around $1.8 billion according to CB insights. These are impressive numbers and the credit can be given to Amazon which changes the perspective of AI integration within retail.

In a nutshell: AI in retail can be explained as a self-learning technology, that with the adequate data, only improves the processes further through smart prediction and much more.

AI solutions are still in the process of growing and progressing. However, there are certain applications within retail that have proven to be fruitful not just in terms of the value it provides as a service but the benefits businesses reap afterward.

What are the top of the line applications of AI in retail? Lets find out.

With digitization, much of the work-load has been automated and streamlined. Now, with the COVID wave placing human contact as harmful, cashier-less stores are an idea that is very much on the table. This idea of lowering the number of human employees working on a store and being replaced by AI-powered robots is not just a concept of the movies anymore.

Amazon is already on the case with Amazon AI introducing stores that are check-out free. You must have heard about Amazon Go and Just Walk Out technology where the items being placed within your trolley are being examined and kept track of, so when you simply walk out of the shop, the Amazon account takes the money. Pretty interesting, right?

AI and IoT play a great role in creating this cashier-less store experience, relieving stores from having expensive operation expenses. With technology like Amazon Go, human staff members are reduced to merely six or so, depending on the size of the store.

The rise of the chatbots was possible due to AI integration, making them capable of conversing in a human-like manner. Moreover, with their ability to understand the query posed by the visitor, they can analyze and provide adequate assistance accordingly.

Safe to say, AI chatbots have elevated customer service, searching, sending notifications, and suggesting relevant products all by themselves. These AI chatbots work wonders in retail as there are so many queries that are lined up mostly filled with product related questions. In addition, they also learn the buying behavior of the customer and suggest products that would match their search and buying intent.

Chatbots are the present and future of retail helping customers navigate through online stores and increasing the revenue of businesses in return.

Voice search is catching up with 31% of smartphone users globally using voice search at least once a week. While, in the year 2020, it is projected to grow to 50%. With Alexa and others, customers can simply ask for the desired product without having to type and visually invest in the process.

Voice search is definitely one of the demanded features in any software solution and software development companies (koderlabs dot com) would incorporate voice and text search to maximize the convenience.

Visual search is a term or technology not too familiar as of yet. However, this AI-powered system enables customers to upload images and find products similar to certain aspects of those uploaded images; like based on color, shapes, and even patterns.

AI coupled with image recognition technology is marvelous and can help significantly in the realm of retail. Imagine wanting a similar dress and just uploading its picture, you get suggestions of places either selling the same or something similar. You then can compare the price difference and go for the one that suits your best.

AI can detect the mood of your customers and provide you with valuable feedback that will allow your representatives to give assistance just in time. Take Walmart as an example. The retail giant has cameras installed at each checkout lane that detects their mood.

If a customer seems annoyed, they would immediately approach and try to help. So, with AI and facial recognition technology, stores can build strong relationships with their customers and ensure their satisfaction.

AI in the retail supply chain can help retailers dodge poor execution and management that leads to major losses. With AI, calculating the demand for a particular product through analyzing the data that includes the history of sales, promotions, location, trends, and various other metrics allow retail stores to make a better future decision.

AI can predict the demand for that certain product and allow you to order just the right amount without having to deal with leftovers or shortage of it.

Since we are currently facing COVID that has placed the necessity of an online-smart-world, AI can predict through the data received from either the websites or mobile apps. Either way, the supply chain is effectively managed and processed systematically.

With the usage of machine learning, the retail industry can easily classify millions of items from various sellers with the right category. For instance, sellers can upload the picture of their product, and machine learning will identify it and classify it accordingly.

Clasification helps automate the mundane and time-consuming task and can be done in a few minutes with the help of AI.

What more is that with such smart classification, customers are able to find the right products under the categories of their choosing.

The retail executives survey conducted by Capgemini at AI in Retail Conference entails that the AI application of technology in retail could potentially save up to $340 billion each year for the industry till 2020. In addition, nearly 80% of these savings will come from supply chain management and return as AI will improve these processes by a large margin.

The global market for AI in retail is projected to grow over $5 million by the year 2022.

Artificial intelligence and Machine Learning-powered software solutions can really change the game for retail, especially amid the pandemic. Not only AI facilitates automation but provides a better insight into businesses by predictive analysis and reporting.

On the customer front, AI-powered chatbots and cashier-less stores provides convenience and futuristic shopping experience with improved customer service.

Although the pandemic has slowed down much of the progress; still, we can see considerable growth in AI-powered solutions geared to improve the retail industry and prep it for the times ahead.

Zubair is a digital enthusiast who loves to write on various trends, including Tech, Software Development, AI, and Personal Development. He is a passionate blogger and loves to read and write. He currently works at Unique Software Development, a custom software development company in Dallas that offers top-notch software development services to clients across the globe.

Read more:

Artificial Intelligence Applications within Retail in 2020 - ReadWrite

Posted in Ai

Artificial intelligence is a totalitarian’s dream here’s how to take power back – The Conversation UK

Individualistic western societies are built on the idea that no one knows our thoughts, desires or joys better than we do. And so we put ourselves, rather than the government, in charge of our lives. We tend to agree with the philosopher Immanuel Kants claim that no one has the right to force their idea of the good life on us.

Artificial intelligence (AI) will change this. It will know us better than we know ourselves. A government armed with AI could claim to know what its people truly want and what will really make them happy. At best it will use this to justify paternalism, at worst, totalitarianism.

Every hell starts with a promise of heaven. AI-led totalitarianism will be no different. Freedom will become obedience to the state. Only the irrational, spiteful or subversive could wish to chose their own path.

To prevent such a dystopia, we must not allow others to know more about ourselves than we do. We cannot allow a self-knowledge gap.

In 2019, the billionaire investor Peter Thiel claimed that AI was literally communist. He pointed out that AI allows a centralising power to monitor citizens and know more about them than they know about themselves. China, Thiel noted, has eagerly embraced AI.

We already know AIs potential to support totalitarianism by providing an Orwellian system of surveillance and control. But AI also gives totalitarians a philosophical weapon. As long as we knew ourselves better than the government did, liberalism could keep aspiring totalitarians at bay.

But AI has changed the game. Big tech companies collect vast amounts of data on our behaviour. Machine-learning algorithms use this data to calculate not just what we will do, but who we are.

Today, AI can predict what films we will like, what news we will want to read, and who we will want to friend on Facebook. It can predict whether couples will stay together and if we will attempt suicide. From our Facebook likes, AI can predict our religious and political views, personality, intelligence, drug use and happiness.

The accuracy of AIs predictions will only improve. In the not-too-distant future, as the writer Yuval Noah Harari has suggested, AI may tell us who we are before we ourselves know.

These developments have seismic political implications. If governments can know us better than we can, a new justification opens up for intervening in our lives. They will tyrannise us in the name of our own good.

The philosopher Isaiah Berlin foresaw this in 1958. He identified two types of freedom. One type, he warned, would lead to tyranny.

Negative freedom is freedom from. It is freedom from the interference of other people or government in your affairs. Negative freedom is no one else being able to restrain you, as long as you arent violating anyone elses rights.

In contrast, positive freedom is freedom to. It is the freedom to be master of yourself, freedom to fulfil your true desires, freedom to live a rational life. Who wouldnt want this?

But what if someone else says you arent acting in your true interest, although they know how you could. If you wont listen, they may force you to be free coercing you for your own good. This is one of the most dangerous ideas ever conceived. It killed tens of millions of people in Stalins Soviet Union and Maos China.

The Russian Communist leader, Lenin, is reported to have said that the capitalists would sell him the rope he would hang them with. Peter Thiel has argued that, in AI, capitalist tech firms of Silicon Valley have sold communism a tool that threatens to undermine democratic capitalist society. AI is Lenins rope.

We can only prevent such a dystopia if no one is allowed to know us better than we know ourselves. We must never sentimentalise anyone who seeks such power over us as well-intentioned. Historically, this has only ever ended in calamity.

One way to prevent a self-knowledge gap is to raise our privacy shields. Thiel, who labelled AI as communistic, has argued that crypto is libertarian. Cryptocurrencies can be privacy-enabling. Privacy reduces the ability of others to know us and then use this knowledge to manipulate us for their own profit.

Yet knowing ourselves better through AI offers powerful benefits. We may be able to use it to better understand what will make us happy, healthy and wealthy. It may help guide our career choices. More generally, AI promises to create the economic growth that keeps us from each others throats.

The problem is not AI improving our self-knowledge. The problem is a power disparity in what is known about us. Knowledge about us exclusively in someone elses hands is power over us. But knowledge about us in our own hands is power for us.

Anyone who processes our data to create knowledge about us should be legally obliged to give us back that knowledge. We need to update the idea of nothing about us without us for the AI-age.

What AI tells us about ourselves is for us to consider using, not for others to profit from abusing. There should only ever be one hand on the tiller of our soul. And it should be ours.

View post:

Artificial intelligence is a totalitarian's dream here's how to take power back - The Conversation UK

Posted in Ai

Hour One raises $5M Seed to generate AI-driven synthetic characters from real humans – TechCrunch

All of the people pictured above are real, but what you are seeing are synthetically generated versions of their real selves. And they can be programmed to say anything. Tech futurists have long warned about humans being replaced by life-like AI-driven figures, where it would be almost impossible to tell between machine and human. Indeed, theres even a new book on this subject of deep fakes.

But that future comes a step closer today with the news that Hour One, which creates AI-driven synthetic characters based on real humans, closes a $5 million seed funding led by Galaxy Interactive (via its Galaxy EOS VC Fund), Remagine Ventures and Kindred Ventures (with participation of Amaranthine).

Hour One will use the funds to scale its AI-driven cloud platform, onboard thousands of new characters and expand its commercial activities.

Founded in 2019, Hour One develops technologies for creating high-quality digital characters based on real people. The idea is to generate production-grade video-based characters in a highly scalable and cost-effective way.The upshot of this is that what appears to be a real human could talk about any product or subject at all, to the point of infinite scale.

This was showcased at its real or synthetic likeness test at CES 2020, challenging people to distinguish between real and synthetic characters generated by its AI.

Oren Aharon, Hour Ones founder and CEO, said in a statement: We believe that synthetic characters of real people will become a part of our everyday life. Our vision is that Hour One will drive the use of synthetic characters to improve the quality of communication between businesses and people across markets and use cases. By enabling each person to create their own character together with our scalable cloud platform, we will provide a variety of solutions for next-gen remote business-to-human interactions.

Hour One is currently working with companies in the e-commerce, education, automotive, communication, and enterprise sectors, with expanded industry applications expected throughout 2020.

The company also showcased its real or synthetic likeness test at CES 2020, challenging people to distinguish between real and synthetic characters generated by its AI.

The real issue, however, is how will this technology be deployed without it being abused.

Lior Hakim, co-founder and CTO, says this potential problem is dealt with via encryption technologies to secure the use and rights of the characters enabling anyone to identify our videos as well as mark them as altered to notify the viewers. The company also says it has an ethical policy code for how its technology is used.

Sam Englebardt, co-founder and managing director of Galaxy Interactive, says the startups ethics-driven approach to the creation of synthetic video is key and that given how challenging production with live actors has become as a result of COVID-19, now is the perfect time for businesses of all sizes to produce their content with Hour Ones synthetic characters.

Clearly this will reduce the cost of synthetic character creation, meaning any textual content could be automatically translated into a live-action video of a person that engages an audience by speaking the text, said Eze Vidra, co-founder and managing partner at Remagine Ventures .

Speaking to TechCrunch, Business strategy lead for Hour One Natalie Monbiot said the company has a unique ability to onboard basically any human being and turn them into a synthetic character thats a lifelike replica of that person. So its not an avatar or a version of that person. It really does look and behave like that person. You can then basically generate new content by uploading new texts. So, for example, in e-commerce, you can pick your characters and get them to present your product or do a product presentation. This means every single product SKU can have its own video presentation.

Excerpt from:

Hour One raises $5M Seed to generate AI-driven synthetic characters from real humans - TechCrunch

Posted in Ai

What Is The Artificial Intelligence Revolution And Why Does It Matter To Your Business? – Forbes

As a species, humanity has witnessed three previous industrial revolutions: first came steam/water power, followed by electricity, then computing. Now, were in the midst of a fourth industrial revolution, one driven by artificial intelligence and big data.

What Is The Artificial Intelligence Revolution And Why Does It Matter To Your Business?

I like to refer to this as the Intelligence Revolution." But whatever we call it the fourth industrial revolution, Industry 4.0 or the Intelligence Revolution one thing is clear: this latest revolution is going to transform our world, just as the three previous industrial revolutions did.

What makes AI so impactful, and why now?

AI gives intelligent machines (be they computers, robots, drones, or whatever) the ability to think and act in a way that previously only humans could. This means they can interpret the world around them, digest and learn from information, make decisions based on what theyve learned, and then take appropriate action often without human intervention. Its this ability to learn from and act upon data that is so critical to the Intelligence Revolution, especially when you consider the sheer volume of data that surrounds us today. AI needs data, and lots of it, in order to learn and make smart decisions. This gives us a clue as to why the Intelligence Revolution is happening now.

After all, AI isnt a new concept. The idea of creating intelligent machines has been around for decades. So why is AI suddenly so transformative? The answer to that question is two-fold:

We have more data than ever before. Almost everything we do (both in the online world and the offline world) creates data. Thanks to the increasing digitization of our world, we now have access to more data than ever before, which means AI has been able to grow much smarter, faster, and more accurate in a very short space of time. In other words, the more data intelligent machines have access to, the faster they can learn, and the more accurate they become at interpreting the information. As a very simple example, think of Spotify recommendations. The more music (or podcasts) you listen to via Spotify, the better able Spotify is to recommend other content that you might enjoy. Netflix and Amazon recommendations work on the same principle, of course.

Impressive leaps in computing power make it possible to process and make sense of all that data. Thanks to advances like cloud computing and distributed computing, we now have the ability to store, process, and analyze data on an unprecedented scale. Without this, data would be worthless.

What the Intelligence Revolution means for your business

I guarantee your business is going to have to get smarter. In fact, every business is going to have to get smarter from small startups to global corporations, from digital-native companies to more traditional businesses. Organizations of all shapes and sizes will be impacted by the Intelligence Revolution.

Take a seemingly traditional sector like farming. Agriculture is undergoing huge changes, in which technology is being used to intelligently plan what crops to plant, where and when, in order to maximize harvests and run more efficient farms. Data and AI can help farmers monitor soil and weather conditions, and the health of crops. Data is even being gathered from farming equipment, in order to improve the efficiency of machine maintenance. Intelligent machines are being developed that can identify and delicately pick soft ripe fruits, sort cucumbers, and pinpoint pests and diseases. The image of a bucolic, traditional farm is almost a thing of the past. Farms that refuse to evolve risk being left behind.

This is the impact of the Intelligence Revolution. All industries are evolving rapidly. Innovation and change is the new norm.Those who cant harness AI and data to improve their business whatever the business will struggle to compete.

Just as in each of the previous industrial revolutions, the Intelligence Revolution will utterly transform the way we do business. For your company, this may mean you have to rethink the way you create products and bring them to market, rethink your service offering, rethink your everyday business processes, or perhaps even rethink your entire business model.

Forget the good vs bad AI debate

In my experience, people fall into one of two camps when it comes to AI. Theyre either excited at the prospect of a better society, in which intelligent machines help to solve humanitys biggest challenges, make the world a better place, and generally make our everyday lives easier. Then there are those who think AI heralds the beginning of the end, the dawning of a new era in which intelligent machines supersede humans as the dominant lifeform on Earth.

Personally, I sit somewhere in the middle. Im certainly fascinated and amazed by the incredible things that technology can achieve. But Im also nervous about the implications, particularly the potential for AI to be used in unethical, nefarious ways.

But in a way, the debate is pointless. Whether youre a fan of AI or not, the Intelligence Revolution is coming your way. Technology is only going in one direction forwards, into an ever-more intelligent future. Theres no going back.

Thats not to say we shouldnt consider the implications of AI or work hard to ensure AI is used in an ethical, fair way one that benefits society as well as the bottom line. Of course, we should do that. But it's important to understand that; however, you feel about it, AI cannot be ignored. Every business leader needs to come to terms with this fact and take action to prepare their company accordingly. This means working out how and where AI will make the biggest difference to your business, and developing a robust AI strategy that ensures AI delivers maximum value.

AI is going to impact businesses of all shapes and sizes, across all industries. Discover how to prepare your organization for an AI-driven world in my new book, The Intelligence Revolution: Transforming Your Business With AI.

Read more here:

What Is The Artificial Intelligence Revolution And Why Does It Matter To Your Business? - Forbes

Posted in Ai

The AI Cosmos Intelligent Algorithms Begin Processing the Universe – The Daily Galaxy –Great Discoveries Channel

This June, 2020, NASA announced that intelligent computer systems will be installed on space probes to direct the search for life on distant planets and moons, starting with the 2022/23 ESA ExoMars mission, before moving beyond to moons such as Jupiters Europa, and of Saturns Enceladus and Titan.

This is a visionary step in space exploration. said NASA researcher Victoria Da Poian. It means that over time well have moved from the idea that humans are involved with nearly everything in space, to the idea that computers are equipped with intelligent systems, and they are trained to make some decisions and are able to transmit in priority the most interesting or time-critical information.

When first gathered, the data produced by the Mars Organic Molecule Analyzer (MOMA) toaster-sized life-searching instrument will not shout out Ive found life here, but will give us probabilities which will need to be analyzed, says Eric Lyness, software lead in the Planetary Environments Lab at NASA Goddard Space Flight Center. Well still need humans to interpret the findings, but the first filter will be the AI system.

Is There Life There, HAL? NASA Announces Intelligent AI Systems Installed on Probes of Distant Planets

Classifying Galaxies

If artificial intelligence can search for alien life, it should be able to distinguish galaxies with spiral patterns from galaxies without spiral patterns, said Ken-ichi Tadaki, at the National Astronomical Observatory of Japan (NAOJ), who came up with the idea that using training data prepared by humans, allowed AI to successfully classify galaxy morphologies with an accuracy of 97.5%. Then applying the trained AI to the full data set, it identified spirals in about 80,000 galaxies.

The NAOJ research group, applied a deep-learning technique, a type of AI, to classify galaxies in a large dataset of images obtained with the Subaru Telescope. Thanks to its high sensitivity, as many as 560,000 galaxies have been detected in the images. It would be extremely difficult to visually process this large number of galaxies one by one with human eyes for morphological classification. The AI enabled the team to perform the processing without human intervention.

To find the very faint, rare galaxies, deep, wide-field data taken with the Subaru Telescope was indispensable, said Dr. Takashi Kojima, about big data captured this June and the power of machine learning that led to the discovery of a galaxy with an extremely low oxygen abundance of 1.6% solar abundance, breaking the previous record of the lowest oxygen abundance. The measured oxygen abundance suggests that most of the stars in this galaxy were formed very recently.

Automated processing techniques for extraction and judgment of features with deep-learning algorithms have been rapidly developed since 2012. Now they usually surpass humans in terms of accuracy and are used for autonomous vehicles, security cameras, and many other applications.

Now that this technique has been proven effective, it can be extended to classify galaxies into more detailed classes, by training the AI on the basis of a substantial number of galaxies classified by humans.

NAOJ is running a citizen-science project GALAXY CRUISE, where citizens examine galaxy images taken with the Subaru Telescope to search for features suggesting that the galaxy is colliding or merging with another galaxy. The advisor of GALAXY CRUISE,

The Subaru Strategic Program is serious Big Data containing an almost countless number of galaxies. Scientifically, it is very interesting to tackle such big data with a collaboration of citizen astronomers and machines, said NAOJ associate professor Masayuki Tanaka. By employing deep-learning on top of the classifications made by citizen scientists in GALAXY CRUISE, chances are, we can find a great number of colliding and merging galaxies.

We are Galactic Babies Something Similar to the AI Revolution May Have Happened at Other Points in the Universe

A First AI Step for Homo Sapiens?

Theres currently an AI revolution, and we see artificial intelligence getting smarter and smarter by the day, Susan Schneider, an associate professor of cognitive science and philosophy at the University of Connecticut who has written about the intersection of SETI and AI, says. That suggests to me something similar may be going on at other points in the universe. Once a society creates the technology that could put them in touch with the cosmos, they are only a few hundred years away from changing their own paradigm from biology to AI.

As SETI Institute astronomer Seth Shostak suggests, looking far into our future planets are volatile, prone to eruptions and earthquakes and the effects of an aging star. Machines arent necessarily going to stay on a planet, he says. Planets are dangerous for machines.

The Daily Galaxy via NAOJ and Goldschmidt Conference

Image Credit: NAOJ/HSC-SSP


The AI Cosmos Intelligent Algorithms Begin Processing the Universe - The Daily Galaxy --Great Discoveries Channel

Posted in Ai

4 Hard-To-Ignore Reasons Why You Should Use AI To Make More Intelligent Products – Forbes

Thanks to the Internet of Things (IoT), artificial intelligence (AI), and advances in sensor technology, a whole host of everyday products are getting smarter. We have smart TVs and smartwatches. We have smart running shoes or rather, smart insoles that gather data on your running performance. You can even get smart nappies that send an alert to your phone when your babys nappy needs changing.

4 Hard-To-Ignore Reasons Why You Should Use AI To Make More Intelligent Products

And thats just the tip of the iceberg. For product manufacturers, theres no doubt weve reached a tipping point in the smart product trend, meaning its no longer possible (or wise) to ignore consumer demand for smart, AI-loaded products.

So, if you aren't already asking yourself, Could our products be improved with AI?, now is the time to start. To whet your appetite, here are four huge benefits of smart products:

1. Making your customers lives easier (and boosting customer satisfaction in the process)

The smart product trend has particularly taken hold in our homes. Because of the IoT, basic home electronic goods and appliances can gather information on whats going on around them and respond accordingly. For example, a smart thermostat can heat your home to the perfect temperature in time for your return from work, without you having to program it. Technology like this makes our homes more efficient, more automated, and more responsive to our needs helping to remove some of the annoying wrinkles and bugbears from everyday life.

This is key to the success of smart products. Rather than inserting AI for AIs sake, its all about solving customers problems and making their lives easier. And I dont just mean in the home. Todays consumers expect smart solutions to a whole host of everyday tasks and activities, including changing their babys nappy and training for a marathon.

2. Building better products (products your customers really want)

Making your products smarter is a fantastic way to build a more in-depth understanding of your customers. This knowledge can and should feed into your product design. In my experience, building a better understanding of customers and developing more desirable products is one of the most attractive benefits for most businesses.

How does this process work? In a nutshell, by building AI capabilities into your products, you have the ability to collect masses of data on your customers habits and preferences: how they use your product, how often they use it, when they typically use it, and more. All this data can be used to improve product design and develop new products that better meet your customers needs.

3. Responding to customers needs more quickly

The customer journey has been forever altered (and sped up) thanks to our permanent attachment to our mobile devices. Because life is so fast-paced these days, were constantly making quick decisions, looking up solutions on-the-fly, and seeking split-second answers to the things we want to know. Google calls these brief I want to know/do/buy/go/learn flashes micro-moments, and, according to Google at least, these micro-moments are becoming a vital part of marketing. In other words, as consumers, we increasingly expect brands to respond instantly and offer us exactly what we want in the here and now.

The more information you have on your customers, the better able you are to spot and respond to these all-important micro-moments. This is where smart products come into play. Because theyre capable of gathering a wealth of data, smart products help you understand your customers actions, preferences, and decision making.

4. Adding new revenue streams

A less obvious benefit of smart products is that they often enable add-on, AI-driven services. And these services can add a lucrative new revenue stream for your business, particularly if you can tailor them into a subscription model. For example, let's say you manufacture security systems and have been transitioning to smart security alarms that gather and transmit data on what's happening in the home. Providers of similar smart security systems are now offering subscriptions to monitor the home in real-time.

Likewise, Apple has transitioned from a straight product manufacturer into a provider of music and TV streaming services (services that are supported by Apples iconic products). In this way, theres a surprising amount of crossover between smart products and smart services. If you can make your own products smarter, it could pave the way for a lucrative move into services.

Everything is becoming smarter dont get left behind

I believe every product-based business must carefully consider this intelligent product trend. Those that dont risk being left behind. Thats not to say you should quickly load your products with unnecessary AI, just so you can label them as smart. As with any new technology, its really important to find those ways in which AI adds the most value. This will be different for each business, and must be driven by your overarching business strategy.

AI is going to impact businesses of all shapes and sizes, across all industries. Discover how to prepare your organization for an AI-driven world in my new book, The Intelligence Revolution: Transforming Your Business With AI.

Visit link:

4 Hard-To-Ignore Reasons Why You Should Use AI To Make More Intelligent Products - Forbes

Posted in Ai

A beginners guide to AI: The difference between video game AI and real AI – The Next Web

Welcome to TNWs beginners guide to AI. This multi-part feature should provide you with a very basic understanding of what AI is, what it can do, and how it works. The guide contains articles on (in order published)neural networks,computer vision,natural language processing,algorithms, and artificial general intelligence.

Among the most common misconceptions surrounding machine learning technology is the idea that video games dating back to the 1970s and 1980s had built-in artificial intelligence capable of interacting with a human user.

If youre curious but in a hurry, video game AI, in the traditional sense, is not what people refer to in the modern era when theyre talking about artificial intelligence. The bots in an online multiplayer game, the enemies in a first-person-shooter, and the CPU-controlled characters in old-school Nintendo games are not examples of artificial intelligence, theyre just clever programming tricks.

Artificial intelligence, in the form we discuss here at Neural, includes machine learning systems like the core neural networks behind Alexa, Siri, and Google Assistant. Adobe uses AI to predict what you want a correction to look like, Google uses it to find you a cheap flight, and Twitter uses AI to determine which ads to serve you.

But, at the risk of confusing things further, video game developers often use AI to create video games. The Unreal Engine, for example, uses AI to allow for real-time graphics rendering. AI is not usually used to control anything that interacts with the player though, because it would typically be a poor solution to most programming problems faced by developers.

When gamers think of the AI in a game, theyre probably not imagining a set of image recognition algorithms. Theyre thinking of the CPU-controlled enemies that can recognize the players actions and respond. Weve seen CPU enemies take cover, call for backup, and respond in kind when players use new tactics. Again, this usually isnt accomplished with artificial intelligence.

Theres only so many things an agent can do in a video game, so its usually more cost-effective and simple to just code an agent to perform certain tasks than it is to train a neural network to control the agent.

Perhaps in the future as games continue to expand in size and features itll begin to make sense to create AI-powered agents to explore video game worlds in tandem with players. One of the most popular forms of developing robust AI systems is to let models loose in video game worlds. StarCraft and Super Mario Bros. are among the most popular gaming worlds for machine learning research.

But the purpose of such research has nothing to do with video game development. Researchers observe AI models in gaming worlds because theyre often physics-based, and that helps AI learn how the real world works.

Though there are some exceptions, we can typically assume any AI reference in the gaming world that refers to the CPUs control over agents ie, the enemy orcs in Shadow of War or the AI-companions in Fallout 4 is not actual artificial intelligence. Though the developers of both games likely used AI for myriad functions in their creation, the games themselves dont have an AI baked-in specifically to control NPCs, agents, monsters, allies, or bad guys.

Published August 10, 2020 21:16 UTC

View original post here:

A beginners guide to AI: The difference between video game AI and real AI - The Next Web

Posted in Ai

Evil AI: These are the 20 most dangerous crimes that artificial intelligence will create – ZDNet

From targeted phishing campaigns to new stalking methods: there are plenty of ways that artificial intelligence could be used to cause harm if it fell into the wrong hands. A team of researchers decided to rank the potential criminal applications that AI will have in the next 15 years, starting with those we should worry the most about. At the top of the list of most serious threats? Deepfakes.

By using fake audio and video to impersonate another person, the technology can cause various types of harms, said the researchers. The threats range from discrediting public figures to influence public opinion, to extorting funds by impersonating someone's child or relatives over a video call.

The ranking was put together after scientists from University College London (UCL) compiled a list of 20 AI-enabled crimes based on academic papers, news and popular culture, and got a few dozen experts to discuss the severity of each threat during a two-day seminar.

SEE: Managing AI and ML in the enterprise 2020: Tech leaders increase project development and implementation (TechRepublic Premium)

The participants were asked to rank the list in order of concern, based on four criteria: the harm it could cause, the potential for criminal profit or gain, how easy the crime could be carried out and how difficult it would be to stop.

Although deepfakes might in principle sound less worrying than, say, killer robots, the technology is capable of causing a lot of harm very easily, and is hard to detect and stop. Relative to other AI-enabled tools, therefore, the experts established that deepfakes are the most serious threat out there.

There are already examples of fake content undermining democracy in some countries: in the US, for example, a doctored video of House Speaker Nancy Pelosi in which she appeared inebriated picked up more than 2.5 million views on Facebook last year.

UK organization Future Advocacy similarly used AI to create a fake video during the 2019 general election, which showed Boris Johnson and Jeremy Corbyn endorsing each other for prime minister. Although the video was not malicious, it stressed the potential of deepfakes to impact national politics.

The UCL researchers said that as deepfakes get more sophisticated and credible, they will only get harder to defeat. While some algorithms are already successfully identifying deepfakes online, there are many uncontrolled routes for modified material to spread. Eventually, warned the researchers, this will lead to widespread distrust of audio and visual content.

Five other applications of AI also made it to the "highly worrying" category. With autonomous cars just around the corner, driverless vehicles were identified as a realistic delivery mechanism for explosives, or even as weapons of terror in their own right. Equally achievable is the use of AI to author fake news: the technology already exists, stressed the report, and the societal impact of propaganda shouldn't be under-estimated.

Also keeping AI experts up at night are applications that will be so pervasive that defeating them will be near impossible. This is the case for AI-infused phishing attacks, for example, which will be perpetrated via crafty messages that will be impossible to distinguish from reality. Another example is large-scale blackmail, enabled by AI's potential to harvest large personal datasets and information from social media.

Finally, participants pointed to the multiplication of AI systems used for key applications like public safety or financial transactions and to the many opportunities for attack they represent. Disrupting such AI-controlled systems, for criminal or terror motives, could result in widespread power failures, breakdown of food logistics, and overall country-wide chaos.

UCL's researchers labelled some of the other crimes that could be perpetrated with the help of AI as only "moderately concerning". Among them feature the sale of fraudulent "snake-oil" AI for popular services like lie detection or security screening, or increasingly sophisticated learning-based cyberattacks, in which AI could easily probe the weaknesses of many systems.

Several of the crimes cited could arguably be seen as a reason for high concern. For example, the misuse of military robots, or the deliberate manipulation of databases to introduce bias, were both cited as only moderately worrying.

The researchers argued, however, that such applications seem too difficult to push at scale in current times, or could be easily managed, and therefore do not represent as imminent a danger.

SEE: AI, machine learning to dominate CXO agenda over next 5 years

At the bottom of the threat hierarchy, the researchers listed some "low-concern" applications the petty crime of AI, if you may. On top of fake reviews or fake art, the report also mentions burglar bots, small devices that could sneak into homes through letterboxes or cat flaps to relay information to a third party.

Burglar bots might sound creepy, but they could be easily defeated in fact, they could pretty much be stopped by a letterbox cage and they couldn't scale. As such, the researchers don't expect that they will cause huge trouble anytime soon. The real danger, according to the report, lies in criminal applications of AI that could be easily shared and repeated once they are developed.

UCL's Matthew Caldwell, first author of the report, said: "Unlike many traditional crimes, crimes in the digital realm can be easily shared, repeated, and even sold, allowing criminal techniques to be marketed and for crime to be provided as a service. This means criminals may be able to outsource the more challenging aspects of their AI-based crime."

The marketisation of AI-enabled crime, therefore, might be just around the corner. Caldwell and his team anticipate the advent of "Crime as a Service" (CaaS), which would work hand-in-hand with Denial of Service (DoS) attacks.

And some of these crimes will have deeper ramifications than others. Here is the complete ranking of AI-enabled crimes to look out for, as compiled by UCL's researchers:

Here is the original post:

Evil AI: These are the 20 most dangerous crimes that artificial intelligence will create - ZDNet

Posted in Ai

Reveal Acquires NexLP to become the leading AI-powered eDiscovery Solution – PR Newswire India

"The future of eDiscovery is artificial intelligence. We've acquired the leader in this space to ensure our platform is powered by cutting-edge AI technology and NexLP's premier data science team," said Reveal CEO, Wendell Jisa. "This exclusive integration of NexLP AI into Reveal's solution provides our clients the opportunity to lead in the evolution of how law is practiced."

NexLP's artificial intelligence platform turns disparate, unstructured data - including email communications, business chat messages, contracts and legal documents - into meaningful insights that can be used to deliver operational efficiencies and proactive risk mitigation for legal, corporate and compliance teams.

Reveal clients have access to the next-generation solution now. The companies have worked to fully integrate NexLP's AI software into Reveal's review software for more than a year. All features, including the industry-exclusive ability to run multiple AI models, as well as all future functionality, become part of Reveal's standard software. NexLP's artificial intelligence platform will remain available as a stand-alone application for current clients.

With the acquisition, Jay Leib, Co-Founder and CEO of NexLP, joins the leadership team of Reveal as its EVP of Innovation & Strategy.

"We chose Reveal, after considering all the major players in the space, because they offer by-far, the most comprehensive, solutions-oriented technology on the market and we have a shared vision for the future of legal technology," said Jay Leib, Reveal EVP of Innovation & Strategy. "Reveal's global footprint and ability to deploy the Reveal solution in the cloud or on-premise enables us to rapidly expand the adoption of AI to tens of thousands of legal, risk and compliance professionals overnight. Our existing clients and partners should all be thrilled with our ability to expand our capabilities by joining Reveal."

The NexLP acquisition is Reveal's second major investment since Gallant Capital Partners, a Los Angeles-based investment firm, acquired a majority stake in Reveal in 2018. In June 2019, Reveal acquired Mindseye Solutions, an industry-leading processing and early case assessment software solution.

About Reveal Data Corporation

Reveal helps legal professionals solve complex discovery problems. As a cloud-based provider of eDiscovery, risk and compliance software, Reveal offers the full range of processing, early case assessment, review and artificial intelligence capabilities. Reveal clients include Fortune 500 companies, legal service providers, government agencies and financial institutions in more than 40 countries across five continents. Featuring deployment options in the cloud or on-premise, an intuitive user design, multilingual user interfaces and the automatic detection of more than 160 languages, Reveal accelerates legal review, saving users time and money. For more information, visit http://www.revealdata.com.

About NexLP

NexLP's Story Engine uses AI and machine learning to derive actionable insight from structured and unstructured data to help legal, corporate and compliance teams proactively mitigate risk and untapped opportunities faster and with a greater understanding of context. In 2014, NexLP was selected to be a member of TechStars Chicago. For more information, visit:http://www.nexlp.com.


Jennifer Fournier[emailprotected]

Photo - https://mma.prnewswire.com/media/1226822/Jisa_and_Leib_Announcement.jpg



Original post:

Reveal Acquires NexLP to become the leading AI-powered eDiscovery Solution - PR Newswire India

Posted in Ai