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Artificial intelligence as of now is considered among the most evident digital technologies and vows to create significant business value later on. Along with a constantly growing body of knowledge, exploration in this field could additionally benefit from fusing innovative features, human characteristics, and organizational objectives into the assessment of artificial intelligence empowered systems.

To bring artificial intelligence systems closer to living beings it is important to understand their mindset and thought process. This can be possible by studying their psychological factors with the help of affordance theory.

Affordances are an idea emerging from the field of perceptual psychology, as a component of Gibsons fundamental work on ecological observation (J. Gibson 1979). An affordance is an activity probability shaped by the connection between an agent and its environment. For instance, the affordance of throwing exists when the catching and pushing capacities of an agent are all well-coordinated with the size and weight of an object.

This throwing capacity isnt a property of either the agent or the object however it is a relationship between them. This relationship-oriented perspective of the potential for activity has an increasing demand in the field of applied sciences, as it presents favorable circumstances for functionality and design over conventional AI methods.

An ecological approach to deal with the plan of robotic agents can hold huge significance for scientists in the field of artificial intelligence. Rendered agents arranged in a physical environment contain an abundance of data, simply by perceiving their general surroundings. By exploiting the connection between the agent and its environment, creators can reduce the requirement for an agent to develop and maintain complex representations; designers can rather concentrate on the details of how the agent interacts directly with the surroundings around it.

The result is increasingly adaptable agents that are better able to react to the dynamic, real-world environment. The ecological approach in this way becomes appropriate for the design of rendered agents, for example, versatile autonomous robots, where the agent might be required to work in complex, unstable, and real-time situations.

Planning and execution in such systems is generally a firmly challenging procedure, with the agent continuously recomputing the best course of short-term activity, along with the execution of the current task. This reduces reliance on a control state that monitors the agents progress in a sequence of activities that may depend on unrealized obsolete information. An ecologically aware agent can exhibit adaptability despite evolving conditions, while still performing complex activities.

Certain experts explain, utilizing a simulated environment, how environmental factors can enable an agent to abort a routine that is no longer relevant, re-perform a failed activity, briefly suspend one task for another, incorporate tasks, and consolidate tasks at the same time to accomplish various objectives.

Comparative attributes have emerged in various physical robotic systems that follow various techniques and configuration patterns, yet incorporate standards compatible with the environmental conditions. Whether physical or simulated, a large number of these systems share a typical methodology in the utilization of exploratory practices, or different stages, in which the agent essentially tries out an activity without a particular objective, to monitor the outcome on its environment.

Through exploratory collaborations, the agent is capable to become familiar with the affordances of its surrounding to a great extent freely. However, the affordances the agent can find will be dependent not only on its physical and perceptual capacities but also on all the types of exploratory practices with which it has been customized.

It is likewise imperative to take into consideration the limitations of this theory. Artificial intelligence analysts are frequently trying to duplicate conduct, which might not possibly emphasize detailed modeling of the systems. The simplicity of usage, speed of execution, and the final performance of the system should all be analyzed when choosing what models to apply to the design of an artificial agent.

In this manner, the reliability of the model utilized will depend upon several elements, including how well the systems are understood, how effectively they can be imitated with the available equipment and programming, and the particular objectives of the examination.

Affordances play multiple roles in situations. On one hand, they permit quicker planning, by diminishing the number of activities available in random circumstances. On the other hand, they encourage increasingly proficient and precise learning of transformation models from data. While researchers and AI specialists may not generally agree on the details of the executions, they share the objective of better understanding agent-environment systems.

Further, huge numbers of the agents being created are moving past the issues of basic navigation and obstacle avoidance, with ecological methodologies being applied to the structure of robots fit for altering the environment with which they connect. It is expected that the utilization of an affordance-based plan will keep on developing alongside the improvement of robotic agents capable of increasingly more complex behaviors.

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Reshaping Artificial Intelligence with the Help of Affordances - Analytics Insight

WISeKey to Showcase its Cybersecurity Solutions for Artificial Intelligence Used in Drones and Robots at SIDO 2020

Drones and robotics sensitive applications are growing thus generating valuable data that has become a target for hackers. WISeKey has developed a comprehensive set of digital security solutions to protect Artificial Intelligence when in use to analyze our environment.

Geneva, Switzerland July 27, 2020: WISeKey International Holding Ltd. (WISeKey) (SIX: WIHN, NASDAQ: WKEY), a leading global cybersecurity and IoT company, announced today that it will be demonstrating its disruptive set of technologies to secure new Artificial Intelligence data acquisition devices, including drones and robots, at the 6th edition of the SIDO leading IoT, AI, Robotics and XR event (Lyon, France September 3-4, 2020).

Big Data is at the heart of our future society construction and management. Connected sensors are invading our environment to acquire a huge amount of data that are analyzed by Artificial Intelligence to predict and make decisions. According to Grand View Research, Inc., estimated the global Artificial Intelligence market reached at US$40B in 2019. The same study forecasts this market to explode to US$733.7B in 2027. Drones and robots are becoming central in this Internet of Things (IoT) revolution. They are therefore exposed to new cyberattacks that steal sensitive data or compromise their firmware and behavior.

Through decades of expertise in cybersecurity, WISeKey has designed complete end-to-end protection solutions for these systems, that could for instance be combined with the IBM Watson IoT offering. IBMs Watson IoT Platform is a cognitive system that learns from and infuses intelligence into the physical world. Device manufacturers and businesses can use the power of Watson IoT Platform to build specialized, integrated solutions to solve their business challenges. Watson IoT Platform implements a messaging broker that allows the exchange of information between devices and business applications, using a secure Public Key Infrastructure (PKI) technology to bring authentication and data encryption. This implies a seamless integration with the WISeKey concept of Root of Trust (RoT) that delivers a digital identity that can be leveraged later in the Watson IoT platform together with WISeKeys solutions such as:

After the recent announcement of our partnership with Parrot, the leading European drone group, to integrate our advanced digital security solutions into the Companys growing range of ANAFI drones, this participation in the SIDO 2020 event will be a new opportunity for WISeKey to explain how our certified digital security can help leading IoT device makers protect their customers assets, stressed Carlos Moreira, Founder and CEO of WISeKey. Our Company has become a key player in the cybersecurity arena, uniquely bringing its Swissness to the protection of Artificial Intelligence.

Want to know more? Come and meet with our experts at SIDO 2020 - Booth E213 Lyon, France September 3-4, 2020 (more information on http://www.sido-event.com and http://www.wisekey.com ). Immediately book your meeting slot at sales@wisekey.com.

About WISeKey

WISeKey (NASDAQ: WKEY; SIX Swiss Exchange: WIHN) is a leading global cybersecurity company currently deploying large scale digital identity ecosystems for people and objects using Blockchain, AI and IoT respecting the Human as the Fulcrum of the Internet. WISeKey microprocessors secure the pervasive computing shaping todays Internet of Everything. WISeKey IoT has an install base of over 1.5 billion microchips in virtually all IoT sectors (connected cars, smart cities, drones, agricultural sensors, anti-counterfeiting, smart lighting, servers, computers, mobile phones, crypto tokens etc.). WISeKey is uniquely positioned to be at the edge of IoT as our semiconductors produce a huge amount of Big Data that, when analyzed with Artificial Intelligence (AI), can help industrial applications to predict the failure of their equipment before it happens.

Our technology is Trusted by the OISTE/WISeKeys Swiss based cryptographic Root of Trust (RoT) provides secure authentication and identification, in both physical and virtual environments, for the Internet of Things, Blockchain and Artificial Intelligence. The WISeKey RoT serves as a common trust anchor to ensure the integrity of online transactions among objects and between objects and people. For more information, visitwww.wisekey.com.

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Disclaimer:This communication expressly or implicitly contains certain forward-looking statements concerning WISeKey International Holding Ltd and its business. Such statements involve certain known and unknown risks, uncertainties and other factors, which could cause the actual results, financial condition, performance or achievements of WISeKey International Holding Ltd to be materially different from any future results, performance or achievements expressed or implied by such forward-looking statements. WISeKey International Holding Ltd is providing this communication as of this date and does not undertake to update any forward-looking statements contained herein as a result of new information, future events or otherwise.This press release does not constitute an offer to sell, or a solicitation of an offer to buy, any securities, and it does not constitute an offering prospectus within the meaning of article 652a or article 1156 of the Swiss Code of Obligations or a listing prospectus within the meaning of the listing rules of the SIX Swiss Exchange. Investors must rely on their own evaluation of WISeKey and its securities, including the merits and risks involved. Nothing contained herein is, or shall be relied on as, a promise or representation as to the future performance of WISeKey.

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WISeKey to Showcase its Cybersecurity Solutions for Artificial Intelligence Used in Drones and Robots at SIDO 2020 - GlobeNewswire

]]]]]]>]]]]>]]> The FLY AI report sets out key steps towards a stronger adoption of AI. Credit: EUROCONTROL.

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British mathematician and scientist Alan Turing first looked into computing intelligence in 1950. In a paper called Computing Machinery and Intelligence, he suggested using a now-famous Imitation Game to test a machines sentient capabilities, which eventually laid the groundwork for the development and discovery of artificial intelligence (AI).

Decades later, AI and its subsets machine learning and deep learning are set to influence the future of many sectors, including aviation. Over the last few years, AI has found a wide array of applications in the industry from ground handling services to airport security and air traffic management (ATM) and there is now scope for more.

This is the argument of the recently-published FLY AI report, which sets out key steps towards a stronger adoption of AI, machine learning and other digital tools in several areas of aviation. Released by the newly formed European Aviation High Level Group on AI, the paper draws upon expertise from key players in the sector. These include leader EUROCONTROL, the Single European Sky ATM Research (SESAR) Joint Undertaking, the International Air Transport Association, Airbus and Airport Council Internationals European division.

Alongside debunking some myths around AI, the paper identifies the most promising areas for its uptake. It also features a FLY AI Action Plan that outlines future measures to better integrate the technology in ATM and other segments of aviation.

AI has been around for more than 60 years but has gained ground more recently, thanks to advances in computing and access to data, comments SESAR JU executive director Florian Guillermet. Machine learning and deep learning are helping to create applications that can learn autonomously and advise on complex problems. Aviation is no stranger to the virtues of AI.

The aviation industry has started to exploit the potential of machine learning algorithms on non-safety critical applications.

In recent times the technology has gained traction in segments such as intelligent maintenance, engineering and prognostics tools, supply chains and customer services. The sector is now eager to find more applications for AI, with some European countries particularly Ireland, Finland, Cyprus, Luxembourg, Sweden and the Netherlands leading the way.

The aviation industry has started to exploit the potential of machine learning algorithms on non-safety critical applications, adds EUROCONTROL head of infrastructure division Paul Bosman. Recently, significant effort has been put on adapting the current certification framework to the specific characteristics of AI applications. With AI, the industrys focus on cybersecurity has also increased.

Based on this assumption, the report aims to demystify and accelerate the use of AI in aviation and ATM in particular, says Guillermet. This field directly involves SESAR, which has been coordinating all EU research and development activities in ATM since 2007 and actively contributed to the production of the report. We also looked at the future evolution in the use of AI, he continues, in particular with the development of joint human-machine cognitive systems.

On the other hand, EUROCONTROL has acted as leader of the project under the European Aviation High Level Group on AI. The group was formed during EUROCONTROLs inaugural conference on AI that took place in May last year.

Guillermet explains that Europe has a strong basis of expertise and knowledge to further develop AI for ATM. Here, automation can help improve operational efficiency in different segments of aviation.

For instance, he says, machine learning digital assistants can mine huge amounts of historical data to support human operators on the ground or in the cockpit to make the best possible decisions.

Within this framework, the FLY AI report identified four areas where AI can help tackle current and future challenges. These are airspace capacity, which is rapidly running out in Europe, the climate change crisis, digital transformation and new levels of complexity in the integration of unmanned aircraft in an already overcrowded airspace.

Machine learning digital assistants can mine huge amounts of historical data to support human operators on the ground or in the cockpit.

AIs ability to identify patterns in complex real-world data that human and conventional computer-assisted analyses struggle to identify makes it extremely well-suited to the aviation sector, says Bosman. AI has the potential to transform aspects of the aviation sector, enabling ATM functions to be performed in entirely different ways in the future.

Bosman also believes that automation can play a pivotal role in improving the industrys environmental credentials. By accelerating the digital transformation in terms of optimising trajectories, creating green routes and increasing prediction accuracy, he says, AI could make a real difference to mitigating the environmental impacts of aviation, in addition to providing decision-makers and experts with new features that could transform the ATM paradigm in terms of new techniques and operating procedures.

In addition, better use of data will help increase and improve predictions with more sophisticated tools, while also boosting the scalability, efficiency and resilience of the current ATM system. Lastly, the technology can enhance safety in segments such as cybersecurity, conflict detection, traffic advisory and resolution tools.

A key takeaway from the report is that stronger cooperation is needed to integrate AI in the existing aviation architecture. The aim is to create an ecosystem involving industry, research institutes, start-ups, policymakers and all relevant stakeholders, in which all conditions are met to progress collectively on this, says Guillermet. No one entity can address it alone.

In response to this need, the reports FLY AI Action Plan looks at the whole value chain from research to implementation and provides stakeholders with a call for action.

According to Bosman, the plan identifies six accelerators that will help achieve this purpose. It is now critical that the community gets together, he adds. A way forward would be to set up a community of practice.

The aim is to create an ecosystem involving industry, research institutes, start-ups, policymakers and all relevant stakeholders.

The first step will be developing a federated data foundation and AI-infrastructure that will grant access to data and enable the creation of an AI aviation partnership. In addition, the European Aviation High Level Group on AI suggests launching specific aviation/ATM training, reskilling/upskilling programmes, change management, a knowledge-based toolbox, and European AI aviation/ATM master classes to share best practices. Awareness and demystification campaigns should also be included.

Meanwhile, the industry will also have to build an AI aviation/ATM community to attract future experts to the sector. This, Bosman concludes, will help consolidate community expertise.

Both EUROCONTROL and SESAR believe that the ongoing coronavirus crisis could help foster automation in the industry despite the challenges that it is causing. The crisis has shown the limits and significant efforts required when using the current manual approach or analytical tools to try to understand the impact of the crisis, predict and help business recovery, says Bosman.

The crisis has shown the limits and significant efforts required when using the current manual approach.

Specifically, AIs reliance on historical data sets on which to train neural networks means that in the event of a second wave of the pandemic, using these data sets will help improve crisis response. As a precaution in the face of such a risk, it makes sense to gather and store all possible data related to the virus and its impact. This is so that they can be used to develop new AI applications that could support the aviation industry in dealing with any future waves of Covid-19 or other pandemics.

In addition, Guillermet says that the pandemic is shining light on the importance of new technologies to help businesses through a crisis. Mastering these technologies and accelerating our plans for a digital Europe sky will deliver an aviation operating environment, which is more resilient, scalable and economically and environmentally sustainable in the long run, he says.

Because aviation is one of the industrial sectors most impacted by the coronavirus pandemic, it should further accelerate the adoption of AI, Bosman concludes. AI can really help transform the industry, provide better decision-making tools, and improve industrial and operational efficiency.

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FLY AI: the role of artificial intelligence in aviation - Airport Technology

The internet is buzzing about the new AI interactive tool which is called Generative Pertained Transformer-3 (GPT-3). This is the third generation of the machine learning model and it can do some amazing things.

The third era of OpenAIs Generative Pretrained Transformer, GPT-3, is a broadly useful language algorithm that utilizes machine learning to interpret text, answer questions, and accurately compose text. It analyzes a series of words, text, and other information then focuses on those examples to deliver a unique output as an article or a picture.

GPT-3 processes a gigantic data bank of English sentences and incredibly powerful computer models called neural nets to recognize patterns and decide its standards of how language functions. GPT-3 has 175 billion learning parameters that empower it to perform practically any task it is assigned, making it bigger than the second-most remarkable language model, Microsoft Corps Turing-NLG algorithm, which has 17 billion learning parameters.

A parameter is a computation in a neural system that applies an extraordinary or lesser weighting to some part of the information, to give that aspect greater or lesser importance in the general estimation of the data.

GPT-3s language abilities are amazing. When appropriately processed by a human, it can compose creative fiction; it can produce working code; it can make sensible business memos; and substantially more. Its possible uses are limited only by our minds.

GPT-3 is much more advanced and evolved than its predecessor. In the year 2019, OpenAI published their discoveries and results on their unaided language model, GPT-2, which was trained in 40Gb texts and was fit for recognizing words in the vicinity. GPT-2, a transformer-based language applied to self-consideration, permitted experts to create exceptionally persuasive and coherent writings.

The system, which is a general-purpose language algorithm, utilized AI to remodel the language processing abilities. However, it created quite a controversy as a result of its capability to create very realistic and reasonable fake news articles dependent on something as simple as an initial sentence, making it inaccessible for the public at first.

At its core, GPT-3 is an incredibly sophisticated text indicator. A human gives it a piece of text as information and the model produces its best investment regarding what the next piece of text should be. It would then be able to repeat this procedure, taking the first information along with the recently produced text, regarding that as new input, and creating a subsequent piece, until it arrives at a length limit.

GPT-3 can figure out how to carry out a task with a single brief, better, at times, than different variants of Transformer that have been calibrated, so to speak, to specifically perform just that task. Subsequently, GPT-3 is the victory of an all-encompassing all-inclusive statement. Simply feed it a huge amount of text till its loads are perfect, and it can proceed to perform entirely well on various specific duties with no further interruption.

A question which most people are asking that why GPT-3 is so hyped? The answer is pretty simple. GPT-3 is trained on a dataset of a large portion of close to a trillion words; therefore GPT-3 can identify and distinguish between the linguistic patterns contained in all that data.

However, there are certain downsides to GPT-3. GPT-3 comes up short on the capacity to reason drastically; it lacks the presence of mind. When confronted with ideas, concepts, or if, the system faces challenges to determine the correct action which needs to be undertaken. It is a demerit to ask GPT-3 basic questions that it cant deal with intelligence.

A related drawback comes from the way that GPT-3 produces its output word-by-word, based on the immediately encompassing text. The outcome is that it can struggle to keep up a rational narrative or convey a meaningful message over a few passages. Compared to humans, who have a steady mental mindset, a perspective that dwells from second to second, from day to dayGPT-3 is amnesiac, constantly straying off confusingly after a couple of sentences.

Keeping the problems aside, GPT-3 has been a major leap in transforming AI by reaching the highest level of human-like intelligence through machine learning. There is no arguing that the GPT-3 has the potential to completely revolutionize the language processing abilities of cognitive systems. The world of AI is constantly evolving and is getting closer to human intelligence day by day. In this scenario, the GPT-3 plays a significant role in understanding human intellect and trying to displace it.

This technology is still in its budding stages and there is a lot of scope for improvement. However, it has surely generated a lot of attention in the industry and it has certainly paved the desire for bigger and better neural networks.

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GPT-3: The Next Revolution in Artificial Intelligence - Analytics Insight

The researchers at De Montfort University Leicester studied attendance data using AI models.

And they say artificial intelligence is the way forward to improve pupil attendance at all schools.

The experts found Monday morning was the most common time for absenteeism at the school, with potential reasons including separation anxiety, taking extended weekend breaks and a lack of motivation to attend school after time off.

To improve attendance, the researchers developed a two-pronged approach.

Firstly, they increased the frequency and value of rewards given for full attendance, holding monthly raffles with prizes for those children who had not missed a day of school during that month.

Secondly, they introduced a Monday Matters initiative, giving pupils fun activities to look forward to on their first day back after the weekend.

Thanks to these measures, Willen Primary achieved the required national average attendance of 96 per cent for the first time in four years. They also saw a huge improvement in persistent absenteeism, which improved by more than 55 per cent compared with a year ago.

Dr Raymond Moodley, a visiting researcher at De Montfort's Institute of AI, was leading the project along with his colleagues Professor Francisco Chiclana, Dr Fabio Caraffini, and Dr Mario Gongora.

Dr Moodley said: By using AI, we were able to pinpoint the problem areas for attendance at Willen Primary School, which in this case was Monday mornings.

One of the key changes we wanted to make was to increase the incentives on offer for pupils who have a 100 per cent attendance record over a shorter time frame. Schools typically tend to reward children on an all or nothing basis by only recognising pupils who have full attendance for the entire year.

However, our approach sets shorter-term goals for the children which makes it more achievable, and if they fail to achieve full attendance in one month, then they can always try again the following month.

Pupils that are persistently absent are typically shown to have the weakest performance at school. Persistent absenteeism is also higher for those children that receive free school meals.

Carrie Matthews, headteacher at Willen Primary School, said: The novel approach provided by the team at the Institute of AI allowed us to improve our understanding of our attendance and put impactful plans in place to reduce absenteeism. The results of our collaboration have been fantastic!

Dr Moodley and his fellow researchers are now developing an easy-to-use AI-enabled absenteeism diagnostic tool that will eventually be available for schools across the UK to download.

Schools will be able to upload their own attendance data from their attendance recording systems and gain insights into their attendance using the AI models.

Attendance is just one parameter measured by Ofsted when it comes to a schools overall performance, said Dr Moodley.

We also want to look at other factors that impact pupil performance and outcomes in later life. These include motivations for choosing subjects particularly girls in STEM (Science, Technology, Engineering, and Mathematics) as well as behavioural management, and optimising the learning environment (classroom layout, timetable design etc.)

AI is tremendously powerful, and we want to harness this power to maximise the overall performance of every child in every school.

Dr Moodley and his colleagues, who are members of a research interest group called RiSE (Research in Societal Enhancement), are currently engaged in a number of AI-led projects in areas including crime prevention, agricultural sustainability, alternative solutions to managing pandemics, and medicine.

For more information about the Institute of AI at De Montfort University visit here

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Four university experts use Artificial Intelligence to discover that children don't like going to school on Mondays in Milton Keynes - Milton Keynes...

Congratulations to the University of Florida andNVIDIAfor their announcement today of a transformational partnership to create higher educations most powerful AI supercomputer and develop a workforce training program that will jumpstart the next generation of discovery and innovation. This initiative will empower students and arm them with new tools to succeed in the 21stcentury economy. The University of Florida is a top-tier public research university, and I commendNVIDIAfor using this opportunity to form this public-private partnership that will exponentially leverage the prior and current investments by the state and federal government in our research at institutions of higher education.

The University of Florida today announced a public-private partnership with NVIDIA that will catapult UFs research strength to address some of the worlds most formidable challenges, create unprecedented access to AI training and tools for underrepresented communities, and build momentum for transforming the future of the workforce.

The initiative is anchored by a $50 million gift -- $25 million from UF alumnus Chris Malachowsky and $25 million in hardware, software, training and services from NVIDIA, the Silicon Valley-based technology company he cofounded and a world leader in AI and accelerated computing.

Along with an additional $20 million investment from UF, the initiative will create an AI-centric data center that houses the worlds fastest AI supercomputer in higher education. Working closely with NVIDIA, UF will boost the capabilities of its existing supercomputer, HiPerGator, with the recently announced NVIDIA DGX SuperPOD architecture. This will give faculty and students within and beyond UF the tools to apply AI across a multitude of areas to improve lives, bolster industry, and create economic growth across the state.

This incredible gift from Chris and NVIDIA will propel the state of Florida to new heights as it strives to be an economic powerhouse, an unrivaled leader in job creation and an international model of 21st-century know-how, Florida Gov. Ron DeSantis said. Over the coming years, tens of thousands of University of Florida graduates with this unique AI-oriented background will create their futures and ours, transforming our workforce and virtually every field and every industry here in Florida and around the world.

With AI holding the potential to revolutionize education and research and indeed every sector of society the University of Florida will work to ensure diversity, equity, and inclusion are at the center of this transformational change. Were thrilledUF is demonstrating such national leadership and placing community engagement and training a 21st Century workforce at the heart of its mission.

"DOE's Artificial Intelligence & Technology Office is ready to partner to accelerate the development of UFs AI curricula and eager to seek UF and NVIDIA expertise on how to advance our own workforce training.

The University of Florida has been at the forefront of these efforts. Its partnership with NVIDIA to develop the HiPerGator3 supercomputer, the most powerful AI machine in US higher education,is a strong symbol of UFs commitment to AI and the growing role it needs to play in how we learn.

The goals UF has set to infuse the entire curriculum with AI training and educational opportunities is an example all universities should aspire to follow. It demonstrates that the University of Florida and NVIDIA understand the depth and the breadth of AIs impact as the demand for AI-literate workers will extend well beyond the tech sector.

As the Co-Chairs of the House AI Caucus, we applaud this new, comprehensive AI program. The initiative includeskey componentsof AI development: computing power, data, talent, and partnerships. We are also pleased to see the focus on equitable access to AI. Executing on this program will mean students at schools across the state and nation including K-12, state and local schools, HBCUs, and HSIs wont be left behind in the AI-driven future. The U.S. must continue to invest in AI, and efforts such as this can help light the way.

UFs National AI Leadership

The partnership will be central to UFs vision to be a national leader in the application of AI, including an expansive plan to elevate its reach and impact in research, teaching, and economic development. It provides a replicable framework for future public-private cooperation, and a model for addressing societys grand challenges through interdisciplinary collaboration. By deploying AI across the curriculum, this powerful resource will address major challenges such as rising seas, aging populations, data security, personalized medicine, urban transportation and food insecurity.

UFs leadership has a bold vision for making artificial intelligence accessible across its campus, said Malachowsky, who serves as an NVIDIA Fellow. What really got NVIDIA and me excited was partnering with UF to go broader still, and make AI available to K-12 students, state and community colleges, and businesses. This will help address underrepresented communities and sectors across the region where the technology will have a profound positive effect.

Extensive Collaboration with NVIDIA

NVIDIAs technology powers two-thirds of the worlds 500 fastest supercomputers, including eight of the top 10. The third-generation HiPerGator will have access to NVIDIAs most advanced AI software and integrate 140 NVIDIA DGX A100 systems with 1,120 NVIDIA A100 Tensor Core GPUs and high-performance NVIDIA Mellanox HDR 200Gb/s InfiniBand networking to deliver 700 petaflops of AI performance.

Artificial intelligence is the most powerful technology force of our time, said Jensen Huang, founder and CEO of NVIDIA. Fueled by data and machine learning, AI is advancing at an exponential pace, impacting every industry from healthcare to transportation to the sciences. Through their generosity and vision, Chris and UF are providing a mighty foundation for students and faculty to harness this technology and drive discovery.

UF is the first institution of higher learning in the U.S. to receive DGX A100 systems, which are designed to accelerate diverse workloads, including AI training, inference, and data analytics.

NVIDIA will also contribute its AI expertise to UF through ongoing support and collaboration across the following initiatives:

As the grand challenges that the scientific and engineering communities face become more complex, the need for artificial intelligence and machine learning to both advance scientific knowledge and manage an increasingly complex technological environment becomes more important. The University of Floridas new supercomputing facility is an important addition to the AI ecosystem and will provide academic researchers with much needed access to computing power that can advance artificial intelligence far into the future.

Unlike traditional high-performance computing systems, this academic supercomputer is specialized to leverage AI simulation and inference, which are important tools in accelerating research areas including drug discovery, astrophysics, and natural hazards modeling. By partnering with NVIDIA, the University of Florida is able to leverage this state-of-the-art technology and use it to enhance the education and training of the next generation of researchers, technicians, and leaders.

This partnership can and should serve as a model of how universities, industry, and government can combine their respective strengths for the benefit of all. This will be vitally important in strengthening the U.S. supercomputing ecosystem, and by extension, U.S. economic competitiveness"

Computing power is at the heart of Artificial Intelligence, and this investment in the fastest AI supercomputer in academia will ensure that the U.S. stays on the cutting edge of research and development. This system, combined with investments in curriculum and critical national, state, and local partnerships, is a model for building an AI economy for all Americans. As the Chair of the Congressional Semiconductor Caucus, Im glad that American semiconductor companies like NVIDIA are leading the charge.

With Director Panchanathan now at the helm of the National Science Foundation, I look forward to seeing his vision for bringing AI supercomputing to other research centers such as the TACC in Austin, Texas.

Integrated AI Curriculum, Intelligent-Decision Support, Equitable Access

As a comprehensive institution, UF has a goal of bringing together students and faculty from across campusand across the state. It will be among the nations first to integrate AI across all disciplines and make it a ubiquitous part of its academic enterprise. It will offer certificates and degree programs in AI and data science, with curriculum modules for specific technical and industry-focused domains. The initiative includes a commitment from UF to hire 100 more faculty members focused on AI. They will join 500 new faculty recently added across disciplines -- many of whom will weave AI into their teaching and research.

More than ever before in my lifetime, people around the country and the globe are looking to universities to expand access to higher education and technology and to level the field of opportunity for all, UF President Kent Fuchs said. UF intends to meet that challenge, and this partnership will help us do it.

Within UF Health, UFs robust academic health center, AI systems are being deployed to monitor patient conditions in real time, making it the first health system to use deep-learning technology to generate patient viability data. Through a novel system known as DeepSOFA, Dr. Azra Bihorac and her team use AI systems to collect and organize a patients medical data so that doctors can make better-informed decisions. DeepSOFA is but one example of how AI technology will be put to use to bolster research and improve patient care at UF Health.

To ensure no community is left behind, UF plans to promote wide accessibility to these computing capabilities and work with other institutions to develop a talent pipeline able to harness the power of AI through several initiatives. These include:

This initiative will allow us to recruit and equip a diverse, talented cadre of faculty and students across multiple disciplines and bring them together with colleagues from government and the private sector to find solutions to our most important problems, said Dr. Cammy Abernathy, dean of UFs Herbert Wertheim College of Engineering.

University officials expect todays announcement will spark additional excitement among others who have significant resources and abilities related to AI, and reaffirmed their commitment to serve as a catalyst for those who wish to step up and join in this amazing adventure.

Steve Orlando July 21, 2020

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UF Announces $70 Million Artificial Intelligence Partnership with NVIDIA - University of Florida

Tyler Cowen is a Bloomberg Opinion columnist. He is a professor of economics at George Mason University and writes for the blog Marginal Revolution. His books include The Complacent Class: The Self-Defeating Quest for the American Dream.

Your AI bartender will serve you now.

Photographer: Leon Neal/Getty Images Europe

Photographer: Leon Neal/Getty Images Europe

This year is likely to be remembered for the Covid-19 pandemic and for a significant presidential election, but there is a new contender for the most spectacularly newsworthy happening of 2020: the unveiling of GPT-3. As a very rough description, think of GPT-3 as giving computers a facility with words that they have had with numbers for a long time, and with images since about 2012.

The core of GPT-3, which is a creation of OpenAI, an artificial intelligence company based in San Francisco, is a general language model designed to perform autofill. It is trained on uncategorized internet writings, and basically guesses what text ought to come next from any starting point. That may sound unglamorous, but a language model built for guessing with 175 billion parameters 10 times more than previous competitors is surprisingly powerful.

The eventual uses of GPT-3 are hard to predict, but it is easy to see the potential. GPT-3 can converse at a conceptual level, translate language, answer email, perform (some) programming tasks, help with medical diagnoses and, perhaps someday, serve as a therapist. It can write poetry, dialogue and stories with a surprising degree of sophistication, and it is generally good at common sense a typical failing for many automated response systems. You can even ask it questions about God.

Imagine a Siri-like voice-activated assistant that actually did your intended bidding. It also has the potential to outperform Google for many search queries, which could give rise to a highly profitable company.

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GPT-3 does not try to pass the Turing test by being indistinguishable from a human in its responses. Rather, it is built for generality and depth, even though that means it will serve up bad answers to many queries, at least in its current state. As a general philosophical principle, it accepts that being weird sometimes is a necessary part of being smart. In any case, like so many other technologies, GPT-3 has the potential to rapidly improve.

It is not difficult to imagine a wide variety of GPT-3 spinoffs, or companies built around auxiliary services, or industry task forces to improve the less accurate aspects of GPT-3. Unlike some innovations, it could conceivably generate an entire ecosystem.

There is a notable buzz about GPT-3 in the tech community. One user in the U.K. tweeted: I just got access to gpt-3 and I can't stop smiling, i am so excited. Venture capitalist Paul Graham noted coyly: Hackers are fascinated by GPT-3. To everyone else it seems a toy. Pattern seem familiar to anyone? Venture capitalist and AI expert Daniel Gross referred to GPT-3 as a landmark moment in the field of AI.

I am not a tech person, so there is plenty about GPT-3 I do not understand. Still, reading even a bit about it fills me with thoughts of the many possible uses.

It is noteworthy that GPT-3 came from OpenAI rather than from one of the more dominant tech companies, such as Alphabet/Google, Facebook or Amazon. It is sometimes suggested that the very largest companies have too much market power but in this case, a relatively young and less capitalized upstart is leading the way. (OpenAI was founded only in late 2015 and is run by Sam Altman).

GPT-3 is also a sign of the underlying health and dynamism of the Bay Area tech world, and thus of the U.S. economy. The innovation came to the U.S. before China and reflects the power of decentralized institutions.

Like all innovations, GPT-3 involves some dangers. For instance, if prompted by descriptive ethnic or racial words, it can come up with unappetizing responses. One can also imagine that a more advanced version of GPT-3 would be a powerful surveillance engine for written text and transcribed conversations. Furthermore, it is not an obvious plus if you can train your software to impersonate you over email. Imagine a world where you never know who you are really talking to Is this a verified email conversation? Still, the hope is that protective mechanisms can at least limit some of these problems.

We have not quite entered the era where Skynet goes live, to cite the famous movie phrase about an AI taking over (and destroying) the world. But artificial intelligence does seem to have taken a major leap forward. In an otherwise grim year, this is a welcome and hopeful development. Oh, and if you would like to read more, here is an article about GPT-3 written by GPT-3.

This column does not necessarily reflect the opinion of the editorial board or Bloomberg LP and its owners.

To contact the author of this story:Tyler Cowen at tcowen2@bloomberg.net

To contact the editor responsible for this story:Michael Newman at mnewman43@bloomberg.net

Before it's here, it's on the Bloomberg Terminal.

Tyler Cowen is a Bloomberg Opinion columnist. He is a professor of economics at George Mason University and writes for the blog Marginal Revolution. His books include The Complacent Class: The Self-Defeating Quest for the American Dream.

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Artificial Intelligence Is the Hope 2020 Needs - Bloomberg

An online app called Amica is now using artificial intelligence to help separating couples make parenting arrangements and divide their assets.

For many people, the coronavirus pandemic has put even the strongest of relationships to the test. A May survey conducted by Relationships Australia found 42% of 739 respondents experienced a negative change in their relationship with their partner under lockdown restrictions.

There has also been a surge in the number of couples seeking separation advice. The Australian government has backed the use of Amica for those in such circumstances. The chatbot uses artificial intelligence (AI) to make suggestions for how splitting couples can divide their money and property based on their circumstances.

But although such tools offer advantages such as convenience and reduced emotional distress, their applications remain limited. And over-relying on them could be a slippery slope.

Read more: Coronavirus: how the pandemic has exposed AIs limitations

According to Amicas website, it considers legal principles and applies them to your circumstances. In other words, the software draws on mass data (collected and embedded by its designers) from similar past cases to make suggestions to users.

Amica demonstrates AIs potential in solving legal problems in family disputes. Interestingly, its not the only tool of this kind in the legal field. There are a range of AI-powered family legal services used in Australia, including Penda and Adieu.

Penda aims to help victims of family violence by providing free legal and safety information. Its AI chatbot provides online legal advice and information without requiring a face-to-face meeting with a lawyer.

Adieu enables couples to achieve amicable financial and parenting agreements via its AI chatbot component Lumi, which can refer couples to mediators, counsellors, lawyers or financial advisers if required. Lumi also has a one-click disclosure tool designed to save time and money by using AI to analyse the financial records of both users.

Australias family law system is overburdened, resulting in long delays for families in the court system. Court proceedings are also expensive, and complex family law cases can cost each party more than A$200,000.

AI tools such as Amica and Adieu enable couples to resolve problems themselves and avoid the slow and expensive court process. This is especially true for couples who have commenced or are considering the separation process now, amid coronavirus restrictions.

Our evaluation of Adieu involved reviewing literature on justice apps and interviewing professionals including mediators, lawyers and financial advisers. We also surveyed 37 Adieu users to find out who would use such an app and how comfortable people were with them.

We found by giving couples dominion over the separation process, they were less likely to be emotionally stressed. Although our survey sample was relatively small, 76% of participants reported not feeling emotional distress. Of those who did, most said this was the result of existing circumstances.

One participant said:

Im pretty new to apps but am learning. Theyre not so bad, but dont really replace people. On the plus side, theyre neutral and dont judge you!

Despite a number of advantages, AI tools for settling legal disputes (much like many other AI tools) come with setbacks.

For instance, theyre not helpful in many cases. Amicas designers highlight the platform is only suitable for amicable separating couples with no complex situations involved, such as family violence. This is because at its current development level, AI-powered chatbots can only generate a relatively simple response from the information theyre given.

According to a 2016 survey by the Australian Bureau of Statistics, around 5.8 million Australians had experienced physical or emotional abuse from a partner.

Read more: Does your AI discriminate?

Further, Australian courts are required to consider each childs best interests when deciding on a family case. There are legitimate concerns that parenting and financial suggestions from AI-powered tools may ignore the needs of children, and only reflect the interests of parents.

There are also concerns around the use of AI in legal family cases more generally. For example, access to online platforms requires a certain amount of digital literacy and accessibility.

This disadvantages people without access to the internet, a smartphone or computer. Also, people may not have the technological skills needed to use apps such as Amica or Adieu.

Apart from family disputes, AI has also been controversially used in criminal cases for sentencing purpose. The COMPAS tool has come under fire on numerous occasions for its use in the US. Its risk assessment algorithms supposedly predict how likely a criminal is to reoffend.

Australias robodebt saga also showed how AI can contribute to problematic administrative decision making. In that debacle, welfare payments made on the basis of self-reported fortnightly income were cross-referenced against an estimated income, taken as an average of annual earnings reported to the Australian Tax Office. This was then used to auto-generate debt notices without human checks.

Read more: From robodebt to racism: what can go wrong when governments let algorithms make the decisions

Its clear AI comes with the potential for embedded bias. As the use of AI-powered technology continues for matters traditionally handled in the courts, a government strategy such as the European Commissions AI White Paper is needed to address the general challenges.

Along with this, an ethical framework with input from Australias legal industry should underpin AI use in the legal sector.

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People are using artificial intelligence to help sort out their divorce. Would you? - The Conversation AU

CHICAGO, July 21, 2020 /PRNewswire/ -- According to the new market research report "Artificial Intelligence in Manufacturing Marketby Offering (Hardware, Software, and Services), Technology (Machine Learning, Computer Vision, Context-Aware Computing, and NLP), Application, End-user Industry and Region - Global Forecast to 2026", published by MarketsandMarkets, the Artificial Intelligence in Manufacturing Marketis expected to be valued at USD 1.1 billion in 2020 and is likely to reach USD 16.7 billion by 2026; it is expected to grow at a CAGR of 57.2% during the forecast period. The major drivers for the market are the increasing number of large and complex datasets (often known as big data), evolving Industrial IoT and automation, improving computing power, and increasing venture capital investments.

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The hardware segment in the AI in manufacturing market projected to grow at highest CAGR during forecast period

The hardware segment is projected to grow at the highest CAGR from 2020 to 2026. Most of the AI hardware manufacturers have been in the business of providing the same hardware components for other technologies such as connected cars, machine vision cameras, and IoT for a long time. This will enable the companies to transfer the technology easily and accordingly develop the AI hardware. Moreover, the increasing participation of startups in AI hardware is complementing the growth of the hardware segment.

Quality control application of AI in manufacturing market to grow at highest CAGR during forecast period

The quality control application is expected to register the highest CAGR during the forecast period. Governments impose regulations on maintaining the quality according to certain benchmarks; for instance, the USD Food and Drug Administration (FDA) imposes stringent guidelines to regulate the quality of pharmaceutical products in accordance with the Current Good Manufacturing Practices (CGMPs). The growing use of robotics and deep learning technology in the manufacturing industry is expected to drive the growth of the AI in manufacturing market for the quality control application

Browsein-depth TOC on"Artificial Intelligence in Manufacturing Market"

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AI in manufacturing market in APAC projected to grow at highest CAGR from 2020 to 2026

The AI in manufacturing market in APAC is expected to grow at the highest CAGR during 20202026. The presence of a large number of industries, especially in countries such as China, India, and Taiwan is resulting in the adoption of AI in the manufacturing sector in APAC. The increasing adoption of AI-based robots is also fueling the market's growth in this region.

NVIDIA Corporation (US), IBM Corporation (US), Alphabet Inc. (Google) (US), Microsoft Corporation (US), Intel Corporation (US), Siemens AG (Germany), General Electric Company (US), General Vision Inc. (US), Progress Software Corporation (US), Micron Technology Inc., (US), Mitsubishi Electric Corporation (Japan), Sight Machine (US), Cisco Systems Inc., (US), and SAP SE (Germany) are the prominent players of AI in manufacturing market.

Related Reports:

Artificial Intelligence in Supply Chain Marketby Offering, Technology, Application (Fleet Management, Supply Chain Planning, Warehouse Management, Virtual Assistant, Freight Brokerage), End-User Industry, and Geography - Global Forecast to 2025

Artificial Intelligence Marketby Offering (Hardware, Software, Services), Technology (Machine Learning, Natural Language Processing, Context-Aware Computing, Computer Vision), End-User Industry, and Geography - Global Forecast to 2025

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Artificial Intelligence in Manufacturing Market Worth $16.7 Billion by 2026 - Exclusive Report by MarketsandMarkets - PRNewswire

In this special guest feature, Joseph E. Mutschelknaus, a director in Sterne Kesslers Electronics Practice Group, addresses some of the top data privacy compliance issues that startups dealing with AI and ML applications face. Joseph prosecutes post-issuance proceedings and patent applications before the United States Patent & Trademark Office. He also assists with district court litigation and licensing issues. Based in Washington, D.C. and renown for more than four decades for dedication to the protection, transfer, and enforcement of intellectual property rights, Sterne, Kessler, Goldstein & Fox is one of the most highly regarded intellectual property specialty law firms in the world.

Last year, the Federal Trade Commission (FTC) hit both Facebook and Google with record fines relating to their handling of personal data. The California Consumer Privacy Act (CCPA), which is widely viewed the toughest privacy law in the U.S., came online this year. Nearly every U.S. state has its own data breach notification law. And the limits of the EUs General Data Protection Regulation (GDPR), which impacts companies around the world, are being tested in European courts.

For artificial intelligence (AI) startups, data is king. Data is needed to train machine learning algorithms, and in many cases is the key differentiator from competitors. Yet, personal data, that is, data relating to an individual, is also subject an increasing array of regulations.

As last years $5 billion fine on Facebook demonstrates, the penalties for noncompliance with privacy laws can be severe. In this article, I review the top five privacy compliance issues that every AI or machine learning startup needs to be aware of and have a plan to address.

1. Consider how and when data can be anonymized

Privacy laws are concerned with regulating personally identifiable information. If an individuals data can be anonymized, most of the privacy issues evaporate. That said, often the usefulness of data is premised on being able to identify the individual that it is associated with, or at least being able to correlate different data sets that are about the same individual.

Computer scientists may recognize a technique called a one-way hash as a way to anonymize data used to train machine learning algorithms. Hash operations work by converting data into a number in a manner such that the original data cannot be derived from the number alone. For example, if a data record has the name John Smith associated with it, a hash operation may to convert the name John Smith into a numerical form which is mathematically difficult or impossible to derive the individuals name. This anonymization technique is widely used, but is not foolproof. The European data protection authorities have released detailed guidance on how hashes can and cannot be used to anonymize data.

Another factor to consider is that many of these privacy regulations, including the GDPR, cover not just data where an individual is identified, but also data where an individual is identifiable. There is an inherent conflict here. Data scientists want a data set that is as rich as possible. Yet, the richer the data set is, the more likely an individual can be identified from it.

For example, The New York Times wrote an investigative piece on location data. Although the data was anonymized, the Times was able to identify the data record describing the movements of New York City Mayor Bill de Blasio, by simply cross-referencing the data with his known whereabouts at Gracie Mansion. This example illustrates the inherent limits to anonymization in dealing with privacy compliance.

2. What is needed in a compliant privacy policy

Realizing that anonymization may not be possible in the context of your business, the next step has to be in obtaining the consent of the data subjects. This can be tricky, particularly in cases where the underlying data is surreptitiously gathered.

Many companies rely on privacy policies as a way of getting data subjects consent to collect and process personal information. For this to be effective, the privacy policy must explicitly and particularly state how the data is to be used. Generally stating that the data may be used to train algorithms is usually insufficient. If your data scientists find a new use for the data youve collected, you must return to the data subjects and get them to agree to an updated privacy policy. The FTC regards a companys noncompliance with its own privacy policy as an unreasonable trade practice subject to investigation and possible penalty. This sort of noncompliance was the basis for the $5 billion fine assessed against Facebook last year.

3. How to provide a right to be forgotten

To comply with many of these regulations, including the GDPR and CCPA, you must provide not only a way for a data subject to refuse consent, but also a way to for a data subject to withdraw consent already given. This is sometimes called a right to erase or a right to be forgotten. In some cases, a company must provide a way for subjects to restrict uses of data, offering data subjects a menu of ways the company can and cannot use collected data.

In the context of machine learning, this can be very tricky. Some algorithms, once trained, are difficult to untrain. The ability to remove personal information has to be baked into the system design at the outset.

4. What processes and safeguards need to be in place to properly handle personal data

Privacy compliance attorneys need to be directly involved in the product design effort. In even big sophisticated companies, compliance issues usually arise when those responsible for privacy compliance arent aware of or dont understand the underlying technology.

The GDPR requires certain companies to designate data protection officers that are responsible for compliance. There also record-keeping and auditing obligations in many of these regulations.

5. How to ensure that data security practices are legally adequate

Having collected personal data, you are under an obligation to keep it secure. The FTC regularly brings enforcement actions against companies with unreasonably bad security practices and has detailed guidelines on what practices it considers appropriate.

In the event of a data breach does occur, you should immediately contact a lawyer. Every U.S. state has its own laws governing data breach notification and imposes different requirements in terms of notification and possibly remuneration.

Collecting personal data is essential part of many machine learning startups. Lack of a well-constructed compliance program can be an Achilles heel to any business plan. It is a recipe for an expensive lawsuit or government investigation that could be fatal to a young startup business. So, a comprehensive compliance program has to be an essential part of any AI/ML startups business plan.

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Top Five Data Privacy Issues that Artificial Intelligence and Machine Learning Startups Need to Know - insideBIGDATA

Shudu Gram is a striking South African model. Shes what fashion likes to call one to watch, with a Balmain campaign in 2018, a feature in Vogue Australia on changing the face of fashion, and a red carpet appearance at the 2019 BAFTAs in a custom Swarovski gown.

Im also a model. Im from Canada, although I live in New York City now. Unlike Shudu, whos considered a new face, Ive been in the business for almost five years. I am also a futurist; I spend a lot of time researching emerging technologies and educating young people about the future of work through my startup WAYE. Also unlike Shudu, Im a real model, and by that I mean Im a real person. Shudus not. Shes a 3D digital construction.

Digital models and influencers are successfully breaking into the fashion industry from every angle. Some have even been signed to traditional modeling agencies. Take Miquela Sousa, a 19-year-old Brazilian American model, influencer, and now musician, who has amassed a loyal following of more than 2 million people on Instagram. Shes collaborated with Prada and Givenchy, has been featured in a Calvin Klein video with Bella Hadid, and she just released a song with singer-songwriter Teyana Taylor this past spring.

Impressive stuff, but theres one thing thats keeping real-life me at ease: Miquela, like Shudu, is a computer-generated image (CGI), not artificial intelligence (A.I.). That means that Miquela and Shudu cant actually do anything on their own. They cant think or learn or offer posing variations independently. But that wont be the case for much longer.

DataGrid is a Japanese tech outfit whose A.I. algorithms directly threaten my job. The company uses generative adversarial networks (GANs), which is a type of machine learning, a subset of A.I. Ill spare you the specifics of the algorithmic details, but in summary, these digital models can offer a vast array of posing options that mimic exactly what we do in e-commerce and commercial modeling. For models like myself, thats how we make most of our money. The German e-commerce giant, Zalando (for which I have modeled almost a dozen times), has published research papers on this technology. It would seem to be only a matter of time until fashion giants jump on board.

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I Am a Model and I Know That Artificial Intelligence Will Eventually Take My Job - Vogue.com

New York, July 23, 2020 (GLOBE NEWSWIRE) -- Reportlinker.com announces the release of the report "Artificial Intelligence in Accounting Market Research Report by Component, by Technology, by Deployment, by Application - Global Forecast to 2025 - Cumulative Impact of COVID-19" - https://www.reportlinker.com/p05913257/?utm_source=GNW

On the basis of Component, the Artificial Intelligence in Accounting Market is studied across Services and Solutions. The Services further studied across Managed Services and Professional Services. The Solutions further studied across Platforms and Software Tools.

On the basis of Technology, the Artificial Intelligence in Accounting Market is studied across Machine Learning and Deep Learning and Natural Language Processing.

On the basis of Deployment, the Artificial Intelligence in Accounting Market is studied across Cloud and On-Premises.

On the basis of Application, the Artificial Intelligence in Accounting Market is studied across Automated Bookkeeping, Fraud and Risk Management, Invoice Classification and Approvals, and Reporting.

On the basis of Geography, the Artificial Intelligence in Accounting Market is studied across Americas, Asia-Pacific, and Europe, Middle East & Africa. The Americas region is studied across Argentina, Brazil, Canada, Mexico, and United States. The Asia-Pacific region is studied across Australia, China, India, Indonesia, Japan, Malaysia, Philippines, South Korea, and Thailand. The Europe, Middle East & Africa region is studied across France, Germany, Italy, Netherlands, Qatar, Russia, Saudi Arabia, South Africa, Spain, United Arab Emirates, and United Kingdom.

Company Usability Profiles:The report deeply explores the recent significant developments by the leading vendors and innovation profiles in the Global Artificial Intelligence in Accounting Market including AppZen Inc, AWS Inc, Deloitte Touche Tohmatsu Limited, IBM Corporation, Kore.ai, Inc., KPMG International Cooperative, Microsoft Corporation, OneUp, OSP Labs, Sage Group, SMACC, UiPath, Vic.ai, Inc, Xero Limited, and YayPay Inc.

FPNV Positioning Matrix:The FPNV Positioning Matrix evaluates and categorizes the vendors in the Artificial Intelligence in Accounting Market on the basis of Business Strategy (Business Growth, Industry Coverage, Financial Viability, and Channel Support) and Product Satisfaction (Value for Money, Ease of Use, Product Features, and Customer Support) that aids businesses in better decision making and understanding the competitive landscape.

Competitive Strategic Window:The Competitive Strategic Window analyses the competitive landscape in terms of markets, applications, and geographies. The Competitive Strategic Window helps the vendor define an alignment or fit between their capabilities and opportunities for future growth prospects. During a forecast period, it defines the optimal or favorable fit for the vendors to adopt successive merger and acquisition strategies, geography expansion, research & development, and new product introduction strategies to execute further business expansion and growth.

Cumulative Impact of COVID-19:COVID-19 is an incomparable global public health emergency that has affected almost every industry, so for and, the long-term effects projected to impact the industry growth during the forecast period. Our ongoing research amplifies our research framework to ensure the inclusion of underlaying COVID-19 issues and potential paths forward. The report is delivering insights on COVID-19 considering the changes in consumer behavior and demand, purchasing patterns, re-routing of the supply chain, dynamics of current market forces, and the significant interventions of governments. The updated study provides insights, analysis, estimations, and forecast, considering the COVID-19 impact on the market.

The report provides insights on the following pointers:1. Market Penetration: Provides comprehensive information on sulfuric acid offered by the key players2. Market Development: Provides in-depth information about lucrative emerging markets and analyzes the markets3. Market Diversification: Provides detailed information about new product launches, untapped geographies, recent developments, and investments4. Competitive Assessment & Intelligence: Provides an exhaustive assessment of market shares, strategies, products, and manufacturing capabilities of the leading players5. Product Development & Innovation: Provides intelligent insights on future technologies, R&D activities, and new product developments

The report answers questions such as:1. What is the market size and forecast of the Global Artificial Intelligence in Accounting Market?2. What are the inhibiting factors and impact of COVID-19 shaping the Global Artificial Intelligence in Accounting Market during the forecast period?3. Which are the products/segments/applications/areas to invest in over the forecast period in the Global Artificial Intelligence in Accounting Market?4. What is the competitive strategic window for opportunities in the Global Artificial Intelligence in Accounting Market?5. What are the technology trends and regulatory frameworks in the Global Artificial Intelligence in Accounting Market?6. What are the modes and strategic moves considered suitable for entering the Global Artificial Intelligence in Accounting Market?Read the full report: https://www.reportlinker.com/p05913257/?utm_source=GNW

About ReportlinkerReportLinker 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.

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The Global Artificial Intelligence in Accounting Market is expected to grow from USD 884.99 Million in 2019 to USD 4,779.11 Million by the end of 2025...

If your phone takes amazing photos, chances are its camera has been augmented by artificial intelligence embedded in the operating system. Now videos are getting the same treatment.

In recent years, smartphone makers have been gradually transforming their cameras into devices that capture data for AI processing beyond what the lens and sensor pick up in a single shot. That effectively turns a smartphone into a professional camera on auto mode and lowers the bar of capturing compelling images and videos.

In an era of TikTok and vlogging, theres a huge demand to easily produce professional-looking videos on the go. Like still images, videos shot on smartphones rely not just on the lens and sensor but also on enhancement algorithms. To some extent, those lines of codes are more critical than the hardware, argued Andreas Lifvendahl, founder and chief executive of Swedish company Imint, whose software now enhances video production in roughly 250 million devices most of which come from Chinese manufacturers.

[Smartphone makers] source different kinds of camera solutions motion sensors, gyroscopes, and so on. But the real differentiator, I would say, is more on the software side, Lifvendahl told TechCrunch over the phone.

Imint started life in 2007 as a spin-off academic research team from Uppsala University in Sweden. It spent the first few years building software for aerial surveillance, just as many cutting-edge innovations that find their first clients in the defense market. In 2013, Lifvendahl saw the coming of widespread smartphone adaptation and a huge opportunity to bring the same technology used in defense drones into the handsets in peoples pockets.

Smartphone companies were investing a lot in camera technology and that was a clever move, he recalled. It was very hard to find features with a direct relationship to consumers in daily use, and the camera was one of those because people wanted to document their life.

But they were missing the point by focusing on megapixels and still images. Consumers wanted to express themselves in a nice fashion of using videos, the founder added.

Source: Imints video enhancement software, Vidhance

The next February, the Swedish founder attended Mobile World Congress in Barcelona to gauge vendor interest. Many exhibitors were, unsurprisingly, Chinese phone makers scouring the conference for partners. They were immediately intrigued by Imints solution, and Lifvendahl returned home to set about tweaking his software for smartphones.

Ive never met this sort of open attitude to have a look so quickly, a clear signal that something is happening here with smartphones and cameras, and especially videos, Lifvendahl said.

Vidhance, Imints enhancement software suite mainly for Android, was soon released. These days, it can enhance precision, reduce motion, track moving objects, auto-correct horizon, reduce noise, and strengthen other aspects of a video in real-time all through deep learning.

In search of growth capital, the founder took the startup public on the Stockholm Stock Exchange at the end of 2015. The next year, Imint landed its first major account with Huawei, the Chinese telecoms equipment giant that was playing aggressive catch-up on smartphones at the time.

It was a turning point for us because once we could work with Huawei, all the other guys thought, Okay, these guys know what they are doing,' the founder recalled. And from there, we just grew and grew.

The hyper-competitive nature of Chinese phone makers means they are easily sold on new technology that can help them stand out. The flipside is the intensity that comes with competition. The Chinese tech industry is both well-respected and notorious for its fast pace. Slow movers can be crushed in a matter of a few months.

In some aspects, its very U.S.-like. Its very straight to the point and very opportunistic, Lifvendahl reflected on his experience with Chinese clients. You can get an offer even in the first or second meeting, like, Okay, this is interesting, if you can show that this works in our next product launch, which is due in three months. Would you set up a contract now?'

Thats a good side, he continued.The drawback for a Swedish company is the demand they have on suppliers. They want us to go on-site and offer support, and thats hard for a small Swedish company. So we need to be really efficient, making good tools and have good support systems.

The fast pace also permeates into the phone makers development cycle, which is not always good for innovation, suggested Lifvendahl. They are reacting to market trends, not thinking ahead of the curve what Apple excels in or conducting adequate market research.

Despite all the scrambling inside, Lifvendahl said he was surprised that Chinese manufacturers could get such high-quality phones out.

They can launch one flagship, maybe take a weekend break, and then next Monday they are rushing for the next project, which is going to be released in three months. So theres really no time to plan or prepare. You just dive into a project, so there would be a lot of loose ends that need to be tied up in four or five weeks. You are trying to tie hundreds of different pieces together with fifty different suppliers.

Imint is one of those companies that thrive by finding a tough-to-crack niche. Competition certainly exists, often coming from large Japanese and Chinese companies. But theres always a market for a smaller player who focuses on one thing and does it very well. The founder compares his company to a little niche boutique in the corner, the hi-fi store with expensive speakers. His competitors, on the other hand, are the Walmarts with thick catalogs of imaging software.

About three-quarters of Imints revenues come from licensing its proprietary software that does these tricks. Some clients pay royalties on the number of devices shipped that use Vidhance, while others opt for a flat annual fee. The rest of the income comes from licensing its development tools or SDK, and maintenance fees.

Imint now supplies its software to 20 clients around the world, including the Chinese big-four of Huawei, Xiaomi, Oppo and Vivo as well as chip giants like Qualcomm and Mediatek. ByteDance also has a deal to bake Imints software into Smartisan, which sold its core technology to the TikTok parent last year. Imint is beginning to look beyond handsets into other devices that can benefit from high-quality footage, from action cameras, consumer drones, through to body cameras for law enforcement.

So far, the Swedish company has been immune from the U.S.-China trade tensions, but Lifvendahl worried as the two superpowers move towards technological self-reliance, outsiders like itself will have a harder time entering the two respective markets.

We are in a small, neutral country but also are a small company, so were not a strategic threat to anyone. We come in and help solve a puzzle, assured the founder.

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Imint is the Swedish firm that gives Chinese smartphones an edge in video production - TechCrunch

Express News Service

BHUBANESWAR: In its bid to ensure greater fiscal accountability, effective resources management and increased productivity, the state government is set to go for integration of artificial intelligence (AI) in the financial auditing mechanism for its organisations in a big way.

The Directorate of Local Fund Audit (DLFA) has started work on an AI project that is aimed at reshaping organisational accountability and bringing about greater trust on Government institutions. It is already seeking advisory services from experts for the successful implementation of the technology infusion in local fund audit automation.

The Government has appointed Professor (Information Systems) of XIMB Sanjay Mohapatra as Advisor to the AI implementation committee of DLFA. He will assist LFA to develop a comprehensive strategy to analyze processes and develop implementation plans for AI-enabled automation and advise on available options and capabilities along with skilling of officials As per the project, the audit of organisations will be conducted using various components of AI like machine learning, deep learning, natural language processing and computer vision.

Combined with other AI components, the technology can classify documents according to type, extract relevant information and perform analysis. The auditors can detect fraud by creating sophisticated machine learning-based modules. The audit can be further transformed by deep learning, which can analyze unstructured data. Data from financial statements can be extracted to calculate proposed materiality based on a range of benchmarks.

AI will help auditors optimise their time, enabling their ability to judge and analyze deeper set of data besides making it possible for them to work smarter and better, said an official involved with the project.The State has 13,606 auditee institutions that are subjected to audit by the LFA organisation. The DLFA, a statutory audit organisation, functioning under the administrative control of Finance department has been undertaking audit of Government, grant-in-aid institutions, Panchayatiraj institutions, Urban Local Bodies, Development Authorities and Universities besides schools, colleges and endowments.

Odisha to use AI in organisation audit in big way

Since auditors face innumerable challenges while evaluating thousands of documents and undertaking investigations on ambiguous transactions, use of AI will enhance their service and accuracy.

After skilling of officials with basic and applied concept of AI in local fund audit, the project will be implemented on a pilot basis before making it universal, the official added.

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Odisha to use artificial intelligence in organisation audit in big way - The New Indian Express

The University of Florida on Tuesday announced a $70 million partnership that will bring artificial intelligence to the forefront of the schools technology programs and introduce the topic more broadly to the student body.

The joint effort with the California-based company NVIDIA will result in the hiring of 100 new faculty and touch every UF graduate with at least one class exposing them to artificial intelligence concepts, the university said. It also will give UF the fastest artificial intelligence supercomputer in higher education, officials said.

The discipline is a branch of computer science that has brought the world products like self-driving cars, food delivery robots and computers that engage humans in a game of chess. Artificial intelligence makes it possible for machines to perform human-like tasks by learning from experience and making adjustments.

Gov. Ron DeSantis, who was present for the Zoom announcement, said the initiative would attract students to UF from across the nation.

I think its going to help the state of Florida modernize our workforce, he said. I think well have an added boost as we seek to become an economic powerhouse and internally as we bounce back from the pandemic.

Provost Joseph Glover said more than 600 faculty and staff members have been involved so far in conversations on how to incorporate the subject of artificial intelligence into their disciplines.

We believe this is so important to our future and our students futures that every student who graduates from the University of Florida, no matter what their major is, should have the opportunity to acquire the knowledge, the training and the tools of artificial intelligence and data science, Glover said. This is not only important to our students but the nation.

Glover said the partnership will help catapult the University of Florida to the forefront of AI and create the next generation of AI-enabled workforce in this country.

The announcement laid out the financial details of the partnership: a $25 million gift from Chris Malachowsky, co-founder of NVIDIA and an alumnus of UFs Herbert Wertheim College of Engineering; $25 million in hardware, software, training and services from NVIDIA; and a $20 million investment from UF.

The supercomputer is expected to be installed by November. Its an upgrade from the existing UF supercomputer known as HiPerGator. Malachowsky said the machine will be among the worlds top 10 supercomputers, capable of processing vast amounts of data that can be used across many fields, including climate research and medicine.

In explaining why the company brought the partnership to Florida, Malachowsky described the state as a living laboratory for some of the really big problems of today for society, with its long coastline, large population, varied demographics and a workforce in need of upscaling to deal with the growing digital divide.

UF is in the process of infusing artificial intelligence into its curricula, Glover said. And, while not everyone will be expected to become a programmer, everyone will have exposure to it and the opportunity to be certified in that area, he said.

Certifications will include courses on ethics, bias and how artificial intelligence applies to certain disciplines. By spring, Glover said, he expects a survey course to be offered, and by next summer the school may offer more specific boot camps in artificial intelligence.

Cammy Abernathy, dean of the College of Engineering, said artificial intelligence is already being applied across disciplines to finding solutions for diabetes and as a means to combat climate change and red tide. Broadening access to the topic can lead to a more equitable society, she said.

Philosophers, lawyers and computer scientists are working together to ensure that AI does not further enforce the biases that are present in our society and does not intrude on the privacy and rights of the public, Abernathy said. These issues of fairness and access are important, so much so that they will be at the heart of this initiative.

Glover said the university will work to provide access to businesses, the K-12 education system, historically black colleges and universities and state and community colleges.

The population in Florida will have opportunities to train on this, Glover said.

Malachowsky said he hopes wealthy alumni in each state consider investing in artificial intelligence at their universities.

Rep. Michael T. McCaul, co-chair of the AI Caucus, said in a statement released by the university that the announcement serves as a model.

Computing power is at the heart of artificial intelligence, and this investment in the fastest AI supercomputer in academia will ensure that the U.S. stays on the cutting edge of research and development, he said.

Mori Hosseini, a member of UFs Board of Trustees and former chair of the Board of Governors, said Tuesdays announcement made him wish he could go back to school.

We are all witnessing a significant moment in history for the state of Florida and the University of Florida, he said.

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UF students, get used to this topic: artificial intelligence - Tampa Bay Times

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It should have been artificial intelligences moment in the sun. With billions of dollars of investment in recent years, AI has been touted as a solution to every conceivable problem. So when the COVID-19 pandemic arrived, a multitude of AI models were immediately put to work.

Some hunted for new compounds that could be used to develop a vaccine, or attempted to improve diagnosis. Some tracked the evolution of the disease, or generated predictions for patient outcomes. Some modelled the number of cases expected given different policy choices, or tracked similarities and differences between regions.

The results, to date, have been largely disappointing. Very few of these projects have had any operational impact hardly living up to the hype or the billions in investment. At the same time, the pandemic highlighted the fragility of many AI models. From entertainment recommendation systems to fraud detection and inventory management the crisis has seen AI systems go awry as they struggled to adapt to sudden collective shifts in behaviour.

Also read: How AI is helping reopen factory floors safely in a pandemic

The unlikely hero emerging from the ashes of this pandemic is instead the crowd. Crowds of scientists around the world sharing data and insights faster than ever before. Crowds of local makers manufacturing PPE for hospitals failed by supply chains. Crowds of ordinary people organising through mutual aid groups to look after each other.

COVID-19 has reminded us of just how quickly humans can adapt existing knowledge, skills and behaviours to entirely new situations something that highly-specialised AI systems just cant do. At least yet.

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We now face the daunting challenge of recovering from the worst economic contraction on record, with societys fault lines and inequalities more visible than ever. At the same time, another crisis climate change looms on the horizon.

At Nesta, we believe that the solution to these complex problems is to bring together the distinct capabilities of both crowd intelligence and machine intelligence to create new systems of collective intelligence.

In 2019, we funded 12 experiments to help advance knowledge on how new combinations of machine and crowd intelligence could help solve pressing social issues. We have much to learn from the findings as we begin the task of rebuilding from the devastation of COVID-19.

In one of the experiments, researchers from the Istituto di Scienze e Tecnologie della Cognizione in Rome studied the use of an AI system designed to reduce social biases in collective decision-making. The AI, which held back information from the group members on what others thought early on, encouraged participants to spend more time evaluating the options by themselves.

The system succeeded in reducing the tendency of people to follow the herd by failing to hear diverse or minority views, or challenge assumptions all of which are criticisms that have been levelled at the British governments scientific advisory committees throughout the pandemic.

In another experiment, the AI Lab at Brussels University asked people to delegate decisions to AI agents they could choose to represent them. They found that participants were more likely to choose their agents with long-term collective goals in mind, rather than short-term goals that maximised individual benefit.

Making personal sacrifices for the common good is something that humans usually struggle with, though the British public did surprise scientists with its willingness to adopt new social-distancing behaviours to halt COVID-19. As countries around the world attempt to kickstart their flagging economies, will people be similarly willing to act for the common good and accept the trade-offs needed to cut carbon emissions, too?

COVID-19 may have knocked Brexit off the front pages for the last few months, but the UKs democracy will be tested in the coming months by the need to steer a divided nation through tough choices in the wake of Britains departure from the EU and an economic recession.

In a third experiment, a technology company called Unanimous AI partnered with Imperial College, London to run an experiment on a new way of voting, using AI algorithms inspired by swarms of bees. Their swarming approach allows participants to see consensus emerging during the decision-making process and converge on a decision together in real-time helping people to find collectively acceptable solutions. People were consistently happier with the results generated through this method of voting than those produced by majority vote.

In each of these experiments, weve glimpsed what could be possible if we get the relationship between AI and crowd intelligence right. Weve also seen how widely held assumptions about the negative effects of artificial intelligence have been challenged. When used carefully, perhaps AI could lead to longer-term thinking and help us confront, rather than entrench, social biases.

Alongside our partners, the Omidyar Network, Wellcome, Cloudera Foundation and UNDP, we are investing in growing the field of collective-intelligence design. As efforts to rebuild our societies after coronavirus begin, were calling on others to join us. We need academic institutions to set up dedicated research programmes, more collaboration between disciplines, and investors to launch large-scale funding opportunities for collective intelligence R&D focused on social impact. Our list of recommendations is the best place to get started.

In the meantime, well continue to experiment with novel combinations of crowd and machine intelligence, including launching the next round of our grants programme this autumn. The world is changing fast and its time for the direction of AI development to change, too.

Kathy Peach, Head of the Centre for Collective Intelligence Design, Nesta

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Also read: Power consumption can explode with increasing use of artificial intelligence

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Covid could have been AIs moment in sun. But it isnt as flexible as humans yet - ThePrint

Artificial intelligence (AI) is at the top of the buzzword bingo reel in the world of tech, and for good reason. Were seemingly shifting from the era of businesses (and the public) talking about AI and marvelling at its mysterious power, to wondering how it can be used to best tackle real-world challenges day to day.

That said, with the fine-tuning of the technology comes increasing attempts to exploit some of its frailties. So just how will the world harness, advance and protect AI technology within the year to come? Here are few of the more edge-case applications of AI taking place.

Advances in deep-learning and AI continue to make deepfakes more realistic. This technology has already proven itself dangerous in the wrong hands; many predict that deepfakes could provide a dangerous new medium for information warfare, helping to spread misinformation or fake news. The majority of its use, however, is in the creation of non-consensual pornography which most frequently targets celebrities, owed to large amounts of data samples in the public domain.Deepfake technology has also been used in highly-sophisticated phishing campaigns.

Beyond illicit ingenuity in shady corners of cyberspace, the fundamental technology is proving itself a valuable tool in a few other disparate places. Gartners Andrew Frank called the technology a potential asset to enterprises in personalized content production: Businesses that utilize mass personalization need to up their game on the volume and variety of content that they can produce, and GANs [Generative Adversarial Network] simulated data can help.

Last year, a video featuring David Beckham speaking in nine different languages for a Malaria No More campaign was released. The content was a result of video manipulation algorithms and represented how the technology can be used for a positive outcome reaching a multitude of different audiences quickly with accessible, localized content in an engaging medium.

Meanwhile, a UK-based autonomous vehicle software company has developed deepfake technology that is able to generate thousands of photo-realistic images in minutes, which helps it train autonomous driving systems in lifelike scenarios, meaning the vehicle makers can accelerate the training of systems when off the road.

The Financial Times also reported on a growing divide between traditional computer-generated graphics which are often expensive and time-consuming and the recent rise in deepfake tech, while Disney used deepfake technology to include the young version of Harrison Ford as Han Solo in the recent Star Wars films.

Facial recognition is enabling convenience, whether its a quick passport check-in process at the airport (remember those?) or the swanky facial software in newer phone models. But AIs use in facial recognition extends now to surveillance, security, and law enforcement. At best, it can cut through some of the noise of traditional policing. At worst, its susceptible to some of its own in-built biases, with recorded instances of systems trained on misrepresentative datasets leading to gender and ethnicity biases.

Facial recognition has been dragged to the fore of discussion, following its use at BLM protests and the wrongful arrest of Robert Julian-Borchak Williams at the hand of faulty AI algorithms earlier this year. A number of large tech firms, including Amazon and IBM,have withdrawn their technology from use by law enforcement.

AI has a long way to go to match the expertise of our human brains when it comes to recognizing faces. These things on the front of us are complex and changeable; algorithms can be easily confused. Theres a roadmap of hope for the format, though, thanks to further advances in deep-learning. As an AI machine matches two faces correctly or incorrectly, it remembers the steps and creates a network of connections, picking up past patterns and repeating them or altering them slightly.

Facial recognitions controversies have furthered discussions around ethical AI, allowing us to clearly understand the tangible impact of misrepresentative datasets in training AI models, which are equally worrying in other applications and use cases, such as recruitment.As the technology is deployed into more and more areas in the world around us, its dependability, neutrality and compliance with existing laws becomes all the more critical.

With every promising advance in technology comes another challenge, and a recent CBInsights paper warns of AIs role in the rise of new-age hacks.

Sydney-based researchers Skylight Cyber reported finding an inherent bias in an AI model developed by cybersecurity firm Cylance, and were able to create a universal bypass that allowed malware to go undetected. They were able to understand how the AI model works, the features it uses to reach decisions, and create tools to fool it time and again. Theres also the potential for a new crop of hackers and malware to poison data corrupting AI algorithms and disrupting the usual detection of malicious/normal network behaviour. This problematic level of manipulation doesnt do a lot for the plaudits that many cybersecurity firms give to products that use AI.

AI is also being used by the attackers themselves. In March last year, scammers were thought to have leveraged AI to impersonate the voice of a business executive at a UK-based energy business, requesting from an employee the successful transfer of hundreds and thousands of dollars to a fraudulent account.More recently, its emerged that these concerns are valid, and not a whole lot of sophistication is required to pull them off. As seen in the case of Katie Jones a fake LinkedIn account used to spy and phish information from her connections an AI-generated image was enough to dupe unsuspecting businessmen into connecting and potentially sharing sensitive information.

Meanwhile, some believe AI-driven malware could be years away if on the horizon at all but IBM has researched how existing AI models can be combined with current malware techniques to create challenging new breeds in a project dubbed DeepLocker. Comparing its potential capabilities to a sniper attack as opposed to traditional malwares spray and pray approach, IBM said DeepLocker was designed for stealth: It flies under the radar, avoiding detection until the precise moment it recognizes a specific target.

Theres no end to innovation when it comes to cybercrime, and we seem set for some sophisticated, disruptive activity to emerge from the murkier shadows of AI.

Automated machine learning, or AutoML (a term coined by Google), reduces or completely removes the need for skilled data scientists to build machine learning models. Instead, these systems allow users to provide training data as an input, and receive a machine learning model as an output.

AutoML software companies may take a few different approaches. One approach is to take the data and train every kind of model, picking the one that works best. Another is to build one or more models that combine the others, which sometimes give better results. Businesses ranging from motor vehicles to data management, analytics and translation are seeking refined machine learning models through the use of AutoML. With a marked shortage of AI experts, this technology will help democratise the tech and cut down computing costs.

Despite its name, AutoML has so far relied a lot on human input to code instructions and programs that tell a computer what to do. Users then still have to code and tune algorithms to serve as building blocks for the machine to get started. There are pre-made algorithms that beginners can use, but its not quite automatic.

Google computer scientists believe they have come up with a new AutoML method that can generate the best possible algorithm for a specific function, without human intervention. The new method is dubbed AutoML-Zero, which works by continuously trying algorithms against different tasks, and improving upon them using a process of elimination, much like Darwinian evolution.

AI and machine learning may be streamlining processes, but they are doing so at some cost to the environment.

AI is computationally intensive (it uses a whole load of energy), which explains why a lot of its advances have been top-down. As more companies look to cut costs and utilize AI, the spotlight will fall on the development and maintenance of energy-efficient AI devices, and tools that can be used to turn the tide by pointing AI expertise towards large-scale energy management.

Artificial Intelligence also has a role in augmenting energy efficiency. Tech giants are using systems that can gather data from sensors every five minutes, and use algorithms to predict how different combinations of actions will positively or negatively affect energy use.

In 2018, Chinas data centers produced 99 million metric tons of carbon dioxide (thats equivalent to 21 million cars on the road). Worldwide, data centers consume 3 to 5 percent of total global electricity, and that will continue to rise as we rely more on cloud-bases services. Savvy to the need to go green, tech giants are now employing AI systems that can gather data from sensors every five minutes, and use algorithms to predict how different combinations of actions will positively or negatively affect energy use. AI tools can also spot issues with cooling systems before they happen, avoiding costly shutdowns and outages for cloud customers.

From low power AI processors in edge technologies to large scale renewable energy solutions (thats AI dictating the angle of solar panels, and predicting wind power output based on weather forecasts), there are positive moves happening as we enter the 2020s. More green-conscious, AI-intensive tech firms are popping all the time, and we look forward to seeing how they navigate the double-edged sword of energy-guzzling AI being used to mitigate the guzzling of energy.

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Exploring the edge cases of artificial intelligence in 2020 - TechHQ

This year is likely to be remembered for the COVID-19 pandemic and for a significant presidential election, but there is a new contender for the most spectacularly newsworthy happening of 2020: the unveiling of GPT-3. As a very rough description, think of GPT-3 as giving computers a facility with words that they have had with numbers for a long time, and with images since about 2012.

The core of GPT-3, which is a creation of OpenAI, an artificial intelligence company based in San Francisco, is a general language model designed to perform autofill. It is trained on uncategorized internet writings, and basically guesses what text ought to come next from any starting point. That may sound unglamorous, but a language model built for guessing with 175 billion parameters 10 times more than previous competitors is surprisingly powerful.

The eventual uses of GPT-3 are hard to predict, but it is easy to see the potential. GPT-3 can converse at a conceptual level, translate language, answer email, perform (some) programming tasks, help with medical diagnoses and, perhaps someday, serve as a therapist. It can write poetry, dialogue and stories with a surprising degree of sophistication, and it is generally good at common sense a typical failing for many automated response systems. You can even ask it questions about God.

Imagine a Siri-like voice-activated assistant that actually did your intended bidding. It also has the potential to outperform Google for many search queries, which could give rise to a highly profitable company.

GPT-3 does not try to pass the Turing test by being indistinguishable from a human in its responses. Rather, it is built for generality and depth, even though that means it will serve up bad answers to many queries, at least in its current state. As a general philosophical principle, it accepts that being weird sometimes is a necessary part of being smart. In any case, like so many other technologies, GPT-3 has the potential to rapidly improve.

It is not difficult to imagine a wide variety of GPT-3 spinoffs, or companies built around auxiliary services, or industry task forces to improve the less accurate aspects of GPT-3. Unlike some innovations, it could conceivably generate an entire ecosystem.

There is a notable buzz about GPT-3 in the tech community. One user in the U.K. tweeted: "I just got access to gpt-3 and I can't stop smiling, i am so excited." Venture capitalist Paul Graham noted coyly: "Hackers are fascinated by GPT-3. To everyone else it seems a toy. Pattern seem familiar to anyone?" Venture capitalist and AI expert Daniel Gross referred to GPT-3 as "a landmark moment in the field of AI."

I am not a tech person, so there is plenty about GPT-3 I do not understand. Still, reading even a bit about it fills me with thoughts of the many possible uses.

It is noteworthy that GPT-3 came from OpenAI rather than from one of the more dominant tech companies, such as Alphabet/Google, Facebook or Amazon. It is sometimes suggested that the very largest companies have too much market power but in this case, a relatively young and less capitalized upstart is leading the way. (OpenAI was founded only in late 2015 and is run by Sam Altman).

GPT-3 is also a sign of the underlying health and dynamism of the Bay Area tech world, and thus of the U.S. economy. The innovation came to the U.S. before China and reflects the power of decentralized institutions.

Like all innovations, GPT-3 involves some dangers. For instance, if prompted by descriptive ethnic or racial words, it can come up with unappetizing responses. One can also imagine that a more advanced version of GPT-3 would be a powerful surveillance engine for written text and transcribed conversations. Furthermore, it is not an obvious plus if you can train your software to impersonate you over email. Imagine a world where you never know who you are really talking to "Is this a verified email conversation?" Still, the hope is that protective mechanisms can at least limit some of these problems.

We have not quite entered the era where "Skynet goes live," to cite the famous movie phrase about an AI taking over (and destroying) the world. But artificial intelligence does seem to have taken a major leap forward. In an otherwise grim year, this is a welcome and hopeful development. Oh, and if you would like to read more, here is an article about GPT-3 written by GPT-3.

Tyler Cowen is a Bloomberg Opinion columnist. He is a professor of economics at George Mason University and writes for the blog Marginal Revolution. His books include "Big Business: A Love Letter to an American Anti-Hero."

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Artificial intelligence is the hope 2020 needs | Commentary | Seattle Times - Walla Walla Union-Bulletin

WASHINGTON - New software developed by BAE Systems could help the Department of Defense build confidence in decisions and intelligence produced by machine learning algorithms, the company claims.

BAE Systems said it recently delivered its new MindfuL software program to the Defense Advanced Research Projects Agency in a July 14 announcement. Developed in collaboration with the Massachusetts Institute of Technologys Computer Science and Artificial Intelligence Laboratory, the software is designed to increase transparency in machine learning systemsartificial intelligence algorithms that learn and change over time as they are fed ever more databy auditing them to provide insights about how it reached its decisions.

The technology that underpins machine learning and artificial intelligence applications is rapidly advancing, and now its time to ensure these systems can be integrated, utilized, and ultimately trusted in the field, said Chris Eisenbies, product line director of the cmpanys Autonomy, Control, and Estimation group. The MindfuL system stores relevant data in order to compare the current environment to past experiences and deliver findings that are easy to understand.

While machine learning algorithms show promise for DoD systems, determining how much users can trust their output remains a challenge. Intelligence officials have repeatedly noted that analysts cannot rely on black box artificial intelligence systems that simply produce a decision or piece of intelligencethey need to understand how the system came to that decision and what unseen biases (in the training data or otherwise) might be influencing that decision.

MindfuL is designed to help address that gap by providing more context around those outputs. For instance, the company says its program will issue statements such as The machine learning system has navigated obstacles in sunny, dry environments 1,000 times and completed the task with greater than 99 percent accuracy under similar conditions; or The machine learning system has only navigated obstacles in rain 100 times with 80 percent accuracy in similar conditions; manual override recommended. Those types of statements can help users evaluate how much confidence they should place in any individual decision produced by the system.

This is the first release of the MindfuL software as part of a $5 million, three-year contract under DARPAs Competency-Aware Machine Learning (CAML) program. BAE Systems plans to demonstrate their software in both simulation and in prototype hardware later this year.

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Could this software help users trust machine learning decisions? - C4ISRNet

The global pandemic is pushing the healthcare system even harder to find ways to help hospitals efficiently address cost and streamline operations. From managing healthcare billing and the insurance process to providing a faster diagnosis of a serious disease, artificial intelligence (AI) has the potential to completely change how hospitals operate. MedTech Intelligence recently discussed some of the areas of impact with Jim McGowan, head of product at ElectrifAI.

MedTech Intelligence: How is AI helping hospitals manage healthcare bills and the insurance process?

Jim McGowan: The original areas within a hospital where AI created efficiency were in registration and insurance processing, most notably in revenue cycle management (RCM). RCM was envisioned as a seamless process across patient appointment and registration; claim coding and submission; payment reconciliation; and appeals. Over time these solutions grew so complex that parallel industries around Pay and Chase emerged, in which providers needed incremental support to capture all their revenue. With margins in the low single digits each dollar counts.

These RCM systems are rule based, which is antiquated AI technology. [Our] RevCaptureAi solution combats the limitations of these traditional revenue cycles with the dynamic intelligence of artificial intelligence (AI) and machine learning (ML) that track, analyze and generate insights about your missed charges. In a billion-dollar health system, just 1% of missed total charges adds up to $10 million in lost revenue. This is the opportunity.

Both providers and payers are implementing chatbots to more efficiently engage with patients/members by automating common support topics like confirming eligibility, getting claims/payment status, scheduling appointments and more. Machine learning is in the early stages of adoption. ElectrifAi has used machine learning to capture missed codes on hospital bills for [more than] five years, and building practical solutions to AI problems for [more than] 15 [years].

ElectrifAIs CEO Edward Scott discusses artificial intelligence and machine learning during the coronavirus crisis in Beating COVID-19 Is a Team SportMTI: How is the technology streamlining medication management? What is its role in managing procedures?

McGowan: Medication errors are still a significant issue in hospitals. EMR solutions were implemented to improve workflow and data capture for a complete patient view. These solutions have reduced adverse drug events (ADEs). Technology has been used to create many checks-and-balances within hospitals, which requires a double-check and scan of a barcode for each patient and medication to validate the drug was prescribed by a physician. There is continued work needed to capture the full patient history as these solutions are hospital system specific, do not include interoperability with the PBM data, and do not share with other hospital systems. Ultimately, a more complete patient system of record may be necessary to ensure that each system connects to each other to share data.

One of the areas where AI in healthcare has shown the most promise is in diagnostics, which can ultimately be leveraged in operating and emergency room settings. Right now, early diagnosis is one of the most important factors in the ultimate outcome of a patients care. AI deep-learning algorithms are being used to shave down the time it takes to diagnose serious illnesses. Our PulmoAi X-ray solution is an example of a tool that amplifies the work of radiologists, who leverage AI to triage cases as emergency rooms and ICUs overflow.AI is being used within healthcare for evidence-based recommendations. AI algorithms ingest collected vitals, lab results, medication orders and comorbidities and produce smarter triage tools.

We have seen growth in digital applications for mental health and virtual assistants to answer patient questions. As telehealth grows, I would not be surprised if the virtual assistants handle increasingly large volumes of questions, significantly greater than live operators. These bots are becoming much more important as the front-end to a telehealth call.

AI and Robotics for laser eye surgery and orthopedic surgeries are growing. AI-based visualizations are exploding in the market. AI is attempting to enter every facet of healthcare.

MTI: What factors should technology developers consider when designing AI solutions for hospitals?

McGowan: There are a number of important factors: Regulatory concerns, community demographics, fitting into existing workflows, technical proficiency of both the hospital personnel and consumers.

Healthcare is a highly regulated industry. HIPAA balances portability with privacy. This is for a very good reason, but has a lot of side effects, like complicating marketing efforts. You cant send an email to a patient telling her its okay to get the hip surgery she canceled when COVID-19 struck, because you cant guarantee someone else wont read it. If you send someone a reminder about their diabetes medication and are too specific in the email, what happens when that email is opened by someone other than the specific patient? Solutions that require you to log into a website to view the information was the evolution during the 2010s and continued to evolve with the growth in depth and sophistication of the mobile app solutions. Inappropriate sharing of data, even within a family, can create legal liability that hampers more specific and appropriate messaging.

When building solutions, AI can enable a very quick solution to the above concerns. Tools like robotic process automation (RPA) and chat bots have allowed providers to quickly create solutions that gather patient information and respond with an appropriate response, even in the patients preferred language. These more natural language conversations guide the patient to a choice without being overly and overtly intrusive.Most importantly, AI and ML people really have to deeply understand their craft if they want to influence medical decisions of any kind. Data science is not just technology development. It requires deep understanding of the problem domain being addressed, as well as statistics, inference, and logic. And data science without exceptional data engineering is useless. There is no magic inside the algorithms. If the data is bad, the results will be bad. Weve seen data systems where almost half the data is inaccurate. Let that sink in. Would you go to a doctor if half the facts in their medical books were wrong? AI solutions start with great data engineering.

Id like to talk directly to the C-Suite in the hospitals for a moment.

Lets discuss the elephant in the room: many hospitals are poorly run businesses, with razor thin margins and inadequate spending controls. These are not financially healthy organizations.

This year we saw 42 hospitals file for bankruptcyso far. All have two things in common: They all had revenue capture solutions, and they all couldnt pay their bills.

First, revenue capture doesnt address your problem: you need elective surgeries. Revenue Capture fixes leaks in your billing process. Hospitals dont go bankrupt because their billing process is too leaky. The revenue isnt coming in. The elective surgeries arent there.

Second, the revenue capture programs you do have use rules-based systems, and those dont work when the rules change. COVID-19 changed the rules. You needed a machine-learning based solution. Rules-based systems have been around since the 1950s. The world has moved on. We have a machine learning based revenue capture solution, and not one hospital using it has gone bankrupt. And still, that should not be your priority right nowthats just a part of getting healthy.

You need to restart elective surgeries. You need to manage your finances.

Customer engagement isnt optional for any other business, and it isnt optional for yours. Machine learning can help.

You also need to get control of your spending. Spend analytics is critical. Again, this is not optional for any business, hospital or not. Machine learning can help.

AIespecially machine learninghelps improve the health of the patient, the financial health of the hospital, and ultimately the health of the community. The pandemic should not be a reason to push off these technologiesits the reason you should embrace them today.

Artificial intelligence and machine learning are proving to be meaningful weapons in our arsenal during the coronavirus crisis.

Change is constant, and we continue to evolve.

A recent paper released by Duke University cites the promise of AI, but urges policy changes in order to bring AI-enabled clinical decision software to fruition.

Expanded designs that enable clinicians to leverage data in making healthcare decisions, but privacy challenges remain.

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Tech Q&A: Artificial Intelligence Has Promise of Streamlining Hospital Processes, Diagnostic Tools - MedTech Intelligence