Getty

Artificial intelligence is a tool used by people all over the world to empower humans to make informed decisions. With a responsible use of AI, humans are able to solve problems faster because artificial intelligence can process more information at a time than a human can.

As climate change, sustainability, and corporate social responsibility become of increased importance for brands across the globe, business, and communications professionals should keep an eye on how emerging technologies, like AI, are being used to solve real-world problems.

Some of the problems AI is solving today are right out of science fiction movies. Waste recognition, space junk, and sustainability are a few of the challenges that artificial intelligence is helping humanity tackle.

Many dystopian movies depict Earth covered in garbage. People live in slums surrounded by trash without anywhere else for it to go. Thankfully, with the help of companies like Greyparrot, that dystopian predicament doesnt have to be humanitys future. Greyparrot is an artificial intelligence company devoted to using AI-powered computer vision software to increase transparency and automation in recycling.

As consumers become more concerned about product sustainability, plus increasing government regulations, companies are finding new ways to create a circular economy. The circular economy is a framework for an economy that is restorative and regenerative by design. Its a process of designing waste out of an economic system so that a product has a continued life, even after its main use. This is key for the waste and recycling industry which has low economic rates of 14% globally because 60% of the solid waste produced each year ends up in open dumps and landfill[s].

Greyparrot AI helps companies work in a circular economy by highlighting inefficiencies in sorting and waste facilities. The artificial intelligence vision can spot items on a conveyer belt faster and with better accuracy than a human. Their system automatically identifies different types of waste, providing composition information and analytics to help facilities increase recycling rates. AI is easy to scale and provides real-time insight and analysis into facilities. AI can turn the 1% of waste that is monitored and analyzed into 100%. Not to mention the opportunities to partner with smart bins and sorting robots. Greyparrot AI does more than bring waste management into the 21st century, it opens the door to AI applications to less well-known sectors of the economy, making our future circular instead of dystopian.The startup was recently named by The Europas Tech Startup Awardsas the hottest startup in Climate Tech/Green Tech startup of 2020.

Other scifi movies often depict humans escaping Earth when it is no longer inhabitable. They blast off into orbit while leaving robots like Wall-E to clean up the mess. But what if we cant launch into the final frontier because theres too much junk in the way for humans to leave Earths orbit? Space junk is a serious worry for many scientists. According to the European Space Agency, 129 million pieces of debris hurtle around Earth. Space debris poses a danger to astronauts navigating orbit, the network of communication and weather satellites, and future space missions.

Thats where AI and technology companies are coming together to tackle spaces biggest problem. NASA created the Deep Asteroid challenge, inviting participants to use machine learning to help humans avoid the same fate as the dinosaurs. Asteroids arent the only Near Earth Object that can come in contact with Earth. There are approximately 1000 comets and space debris which pose a real danger to life on Earth.

Detecting space junk is one thing but removing it from orbit is another. The startup StartRocket has a plan to remove debris using foam. StartRocket wants to deploy a satellite called Foam Debris Catcher. When the satellite reaches a debris cloud, it would splay out a web of space-grade polymeric foam arms which, according to StartRocket, can capture as much as a ton of space junk. The orbital drag of the foamed junk would pull the mass towards Earth, burning up in the atmosphere.

Artificial intelligence is more than a good samaritan. It offers a big ROI for companies who use it. In the food industry, AI could help generate up to $127 billion a year by 2030. Food waste is a problem before it even hits consumers refrigerators. About one third (about 1.3 billion tons) of food is lost or wasted each year from farm to refrigerator.

One company, Winnow Solutions, uses artificial intelligence to identify and weigh food waste in commercial kitchens. Winnow Vision, the companys AI, automatically assigns a dollar value to each scrapped plate thats dumped into its smart waste bin. Winnow Vision can identify waste foods correctly more than 80 percent of the time and is improving as it learns, which is up to 10% more accurate than kitchen staff (who are usually busy with other work).

A worker throws away expired food in a local supermarket in Brussels.

AI isnt just for the kitchen. Israeli startup, Wasteless, developed a dynamic pricing algorithm for perishable products. Wasteless is able to track an items price in real-time and adjust it based on the expiration date. Wasteless integrates with a stores inventory management system to automatically discount items with shorter shelf lives. By using Wasteless, a store retailer reduced food waste by 39 percent while boosting revenues by 110 percent and still maintaining a positive net margin.

The bad news is that water is a finite resource, but happily, it also happens to be renewable. Unfortunately, more than 25% of water is wasted due to leaks - and thats in commercial buildings alone. Water intelligence company, WINT, developed an AI to detect and stop leaks at the source. WINT does this by using pattern matching to detect water leaks. WINTs AI continually learns and adapts to a companys water network to optimize leak and waste detection. That 25% of water wasted due to leaks? Not for companies who use artificial intelligence. One company reported that WINT reduced its water consumption by 24%. Thats a technology thats good for the budget and the environment.

Artificial Intelligence can analyze, predict, and recommend action. However, its up to people to make the final call and change the systems in place to make the most out of the way we produce. Space junk, food waste, trash, and water leaks are all ways to rethink an industrys purpose. Economics can evolve along with technology, like artificial intelligence, to create sustainable solutions that at times can increase ROI, but can also show a brands goodwill and help that company find its purpose.

The rest is here:

How 4 Companies Are Using AI To Solve Waste Issues On Earth & In Space - Forbes

Theres a good chance youve seen a drone swarm. Maybe not in person, but probably televised during a New Years celebration. A drone swarm occurs when a large number of the flying robots take to the skies in sync.

It isnt a coincidence that they almost always fly in open outdoor areas. For these robotic fliers, it can be difficult to navigate in tight spaces without running into each other or environmental obstacles.

Now, a new machine learning algorithm is helping solve that challenge. Developed by researchers at Caltech, the Global-to-Local Safe Autonomy Synthesis (GLAS) artificial intelligence (AI) allows drone swarms to navigate crowded, unmapped environments.

Typical drone swarm navigation systems work by relying on existing maps of an area. For instance, a group of drones flying to light up the sky for New Years would have a dedicated map of the area above Times Square (or wherever it was flying). It also counts on knowing the route of every drone in the swarm. This helps each unit stay on track and avoid collisions.

The new GLAS program works differently. Thanks to its machine learning capabilities, it lets drones navigate an unknown area on their own while simultaneously communicating with other drones in the swarm. Using this decentralized model makes it possible for the drones to improvise.

It also makes it easier to scale the drone swarm. Since the computing power is spread across many robots, adding more of them is actually helpful.

While the main AI helps the drones navigate their environment, a secondary tracking controller called Neural-Swarm helps them compensate for aerodynamic interactions. This could be something like the downward force of air coming from a drone flying overhead. The system is more sophisticated than many controllers available today since they dont account for aerodynamics.

While drone swarms are almost exclusively used today for entertainment, that will change in the future. Drones are simply too helpful and too versatile to only be used for light shows. As such, the new swarm monitoring technology has plenty of applications.

While light shows will certainly benefit from it, other areas have even more to gain.

Search and rescue is a field that would certainly see improvements. Equipped with the new software, first responders could deploy a swarm of drones to quickly and effectively cover an area. This is only possible because the GLAS program allows them to operate without relying on a map of a predetermined area.

Meanwhile, the tech could be helpful in areas that dont include flying. For example, it could be integrated into self-driving cars to help avoid crashes and traffic jams. If every car included such technology, they would be able to autonomously interact with each other and adjust accordingly.

Despite these exciting applications, it will probably be several years before systems like GLAS are integrated into real-world drone swarms. Much more testing is needed to ensure it is reliable. Still, thanks to innovations like this, drone swarms will one day be commonplace.

Continued here:

AI is helping drone swarms fly in unknown locations - The Burn-In

While we have all been focused on facial recognition as the poster child for AI ethics, another concerning form of AI has quietly emerged and rapidly advanced during COVID-19: AI-enabled employee surveillance at home. Though we are justifiably worried about being watched while out in public, we are now increasingly being observed in our homes.

Surveillance of employees is hardly new. This started in earnest with scientific management of workers led by Frederick Taylor near the beginning of the 20th century, with time and motion studies to determinetheoptimal way to perform a job. Through this, business management focused on maximizing control over how people performed work. Application of this theory extends to the current day. A 2019reportfrom the U.C. Berkeley Labor Center states that algorithmic management introduces new forms of workplace control, where the technological regulation of workers performance is granular, scalable, and relentless.There is no slacking off while you are being watched.

Implementation of such surveillance had existed primarily in factory or warehouse settings, such as at Amazon. Recently, the Chinese Academy of Sciences reported that AI is being used on construction sites. These AI-based systems can offer benefits to employees by using computer vision to check whether employees are wearing appropriate safety gear, such as goggles and gloves, before giving them access to a danger area. However, there is also a more nefarious use case. The report said the AI system with facial recognition was hooked up to CCTV cameras and able to tell whether an employee was doing their job or loitering, smoking or using a smartphone.

Last year, Gartner surveyed 239 large corporations and found that more than 50% were using some type of nontraditional monitoring techniques of their workforce. These included analyzing the text of emails and social-media messages, scrutinizing who is meeting with whom, and gathering of biometric data.A subsequent Accenture surveyof C-suite executives reported that 62% of their organizations were leveraging new tools to collect data on their employees. One monitoring software vendor has noted that every aspect of business is becoming more data-driven, including the people side. Perhaps its true, as former Intel CEO Andy Grove famously stated, that only the paranoid survive.

With the onset of COVID-19 and many people working remotely, some employers have turned to productivity management software to keep track of what employees are doing while they work from home. These systems have purportedly seen a sharp increase in adoption since the pandemic began.

A rising tide of employer worry appears to be lifting all the ships. InterGuard, a leader in employee monitoring software claims three to four times growth in the companys customer base since COVID-19s spread in the U.S. Similarly, Hubstaff and Time Doctor claim interest has tripled. Teramind said 40% percent ofits current customers have addedmore user licenses to their plans. Another firm, aptly named Sneek, said sign-ups surged tenfold at the onset of the pandemic.

The software from these firms operates by tracking activities, whether it is time spent on the phone, the number of emails read and sent, or even the amount of time in front of the computer as determined by screen shot captures, webcam access, and number of keystrokes. Some algorithmically produce a productivity score for each employee that is shared with management.

Enaibleclaims its remote employee monitoring Trigger-Task-Time algorithm is a breakthrough at the intersection of leadership science and artificial intelligence. In an op-ed, the vendor said its software empowers leaders to lead more effectively by providing them with necessary information. In this respect, it appears we have advanced from Taylorism mostly in sophistication of the technology. A university research fellow shared a blunt assessment, saying these are technologies of discipline and domination they are ways of exerting power over employees.

While the ever-present push for productivity is understandable on one level managers have a right to make reasonable requests of workers about their productivity and to minimize cyber-loafing such intense observation opens yet another front in the AI-ethics conversation, especially concerns regarding the amount of information collected by monitoring software, how it might be used, and the potential for inherent bias in the algorithms that would influence results.

Monitoring of employees is legal in the U.S. down to the keystroke. based on the Electronic Communications Privacy Act of 1986. But were now living in an age where monitoring those employees means monitoring them at home which is supposed to be a private environment.

In the 1921 dystopian Russian novelWe that may have influenced the later1984, all of the citizens live in apartments made entirely of glass to enable perfect surveillance by the authorities. Today we already have AI-powered digital assistants such as Google Home and Amazon Alexa that can monitor what is said at home, though allegedly only after they hear the wake word. Nevertheless, there are numerous examples of these devices listening and recording other conversations and images, prompting privacy concerns. With home monitoring of employees, we have effectively turned our work computers into another device with eyes and ears without requiring a wake word adding to home surveillance. These tools can track not only our work interactions but what we say and do on or near our devices. Our at-home lifestyles and non-work conversations could be observed and translated into data that risk managers such as insurers or credit issuers might find illuminating, should employers share this content.

Perhaps work-from-home surveillance is now a fait accompli, an intrinsic part of the modern Information Age that risks the right to privacy of employees within their homes, as well as the office. Already there are employee surveillance product reviews in mainstream media, normalizing the monitoring practice. Nevertheless, in a world where boundaries between work and home have already blurred, the ethics of using AI technologies to monitor employees every move in the guise of productivity enhancement could be a step too far and another topic for potential regulation. The constant AI-powered surveillance risks turning the human workforce into a robotic one.

Gary Grossman is the Senior VP of Technology Practice atEdelmanand Global Lead of the Edelman AI Center of Excellence.

More:

Work-at-home AI surveillance is a move in the wrong direction - VentureBeat

Watch all the Transform 2020 sessions on-demand right here.

Today marked the conclusion of VentureBeats Transform 2020 summit, which took place online for the first time in our history. Luminaries including Google Brain ethicist Timnit Gebru and IBM AI ethics leader Francesca Rossi spoke about how women are advancing AI and leading the trend of AI fairness, ethics, and human-centered AI. Twitter CTO Parag Agrawal detailed the social networks efforts to apply AI to detect fake or hateful tweets. Pinterest SVP of technology Jeremy King walked through learnings from Pinterests explorations of computer vision to create inspirational experiences. And Unity principal machine learning engineer Cesar Romero brought clarity to the link between synthetic data sets and real-world AI model training.

Thats just a sampling of the panels, interviews, and discussions to which Transform 2020 attendees had front-row seats this week. But the sessions that caught my eye were those touching on practical, tangible AI applications as opposed to theoretical. Research remains crucial to the fields advancement, and theres no sign its slowing the over 1,000 papers accepted to ICML 2020 suggest the contrary. However, production environments are perhaps the best opportunity to battle-test proposed tools and algorithms for robustness. Outcome predictions are just that: predictions. It takes real-world experimentation to know whether hypotheses will truly pan out.

Barak Turovsky, Google AI director of product for the natural language understanding team, elucidated steps Google took to mitigate gender bias from the language models powering Google Translate. Leveraging three AI models to detect gender-neutral queries and generate gender-specific translations before checking for accuracy, Googles system can provide multiple responses to translations of words like nurse and let users choose the best one (e.g., the masculine enfermero or the feminine enfermera). Google is a leader in artificial intelligence, and with leadership comes the responsibility to address a machine learning bias that has multiple examples of results about race and sex and gender across many areas, including conversational AI, Turovsky said.

Like Google, software company Cloudera doubled down on productization of its AI and ML technologies. Senior director of engineering Adam Warrington said it deployed a chatbot to improve customer question-and-answer experiences in under a month, leveraging proprietary data sets of client interactions, community posts, subject-matter expert guidance, and more. The underpinning models can understand relevant words and sentences within a support case and extract the right solution from the best source, whether a knowledge base article, product documentation, or community post.

For Yelp, deployment is a core part of the experimentation process, enabled by the companys Bunsen platform. Using Bunsen through a frontend user interface called Beaker, data scientists, engineers, execs, and even public relations reps can determine whether products and models have any negative impact on the growth of business metrics or if theyre meeting goals. Yelp employees get the scale of being able to deploy a model to a cohort of users depending on how they want to reach them, as well as the flexibility to determine if the functionality is perhaps not optimal or, worst-case scenario, is harmful. We have a rapid way of turning those experiences off and doing what we need to do to fix them on the backend, Yelp head of data science Justin Norman told VentureBeat. One of the best things about what Bunsen allows us to do is to scale at speed.

When it comes to practical uses of AI and machine learning in the financial sector, Visa is at the forefront with projects that demonstrate the potential of these technologies. As a rule, the company looks for use cases where AI and ML could deliver at least a 20% to 30% efficiency increase. Its Visa Advanced Authorization platform is a case in point: It uses recurrent neural networks along with gradient boosted trees to determine the likelihood transactions are fraudulent. Melissa McSherry, a senior vice president and global head of data for Visa, said the company prevents $25 billion in annual fraud thanks to the AI it developed. We have definitely taken a use case approach to AI, she said. We dont deploy AI for the sake of AI. We deploy it because its the most effective way to solve a problem.

AI has a role to play in health care, as well. CommonSpirit Health, the largest not-for-profit health care provider in the country, is applying models to optimize the rounds its doctors and nurses make every day. Based upon our thousands of analysis of patients, [if] we dont address the patient in room seven first, theyre going to have to stay longer than they would need to otherwise, chief strategic innovation officer Rich Roth explained. Using AI that way, really to accelerate our workflow, and to clearly show to our caregivers the clinical benefit of why that data is important, is a great example of how technology can help enhance care.

For AI coverage, send news tips toKhari JohnsonandKyle Wiggers and be sure to subscribe to the AI Weekly newsletterand bookmark ourAI Channel.

Thanks for reading,

Kyle Wiggers

AI Staff Writer

Read more:

AI Weekly: Transform 2020 showcased the practical side of AI and ML - VentureBeat

In a quest to decode what keeps the world moving, enterprises across the world are baffled. It is not precious metals or even cryptocurrency it is data. The adage that data is the new oil holds true and soon, every company in the world will either buy or sell data, and the value of this corporate asset would gain prominence with each passing day.

Data fuels digital transformation that drives a mammoth disruption across all industries. It is the key differentiator, coming at a massive speed characterised by volume, variety, velocity and veracity in a very live environment.

The question remains unanswered, how do enterprises gain the most from this valuable resource? The answer is through Data Digitalization. As the name suggests, data digitization is the process by which physical or manual data files like text, audio, images, video are converted into digital forms. The perks of digitized data are plenty, starting from-

The initial step of data digitalization starts with the identification of data needs based on client requirements. This is based on system analysis, requirement specifications and system design.

After an enterprise assesses its data requirements, the next step is to develop a technology roadmap and send it for approval and testing. After the technology roadmap is approved, the data source chart is developed, which may include a printed format converted into a digital format.

The old images are scanned, while the faded images are recovered using advanced digital correction software. The sound and video data are retrieved through the data capturing software which is ultimately converted in the digital format.

The printed documents are checked for physical accuracy. The process imbibes Optical Character Recognition (OCR) software scanning, where the output is checked manually by proof-readers, subsequently converted into PDF, MS-Word, ASCII and HTML formats.

The takeaways from Data Digitalization

Into Data digitization, the physical documents are uploaded online and scanned to a virtual digital medium, which is then digitized to a high-quality format and structured according to the customers needs.

Enterprises can also opt for cases in which these documents are transferred to an electronic archive which complies with the stringent security requirements and provides an option to manage data round the clock from any computer anywhere in the world using web applications.

In a crux, leveraging the power of cognitive computing algorithms, enterprises can synthesize raw data from various information sources, weigh multiple options to arrive at conclusive answers. To achieve this, cognitive systems encapsulate self-learning models using data mining, pattern recognition and natural language processing (NLP) algorithms.

For enterprises to use data digitalization systems they require vast amounts of structured and unstructured data, fed to machine learning algorithms. Over time, these cognitive systems refine the way they identify patterns, process data to become self-sufficient anticipate new problems and possible alternative solutions for the model.

While AI relies on algorithms for problem-solving, patten identification from the hidden data, cognitive computing systems have the loftier goal of creating models that mimic the brains reasoning process to solve the modern concerns of data digitalization and data adaptability.

The increased resilience towards data and digitalization will change the world of data forever. Is your organisation ready with its own digitalized data pipelines?

Share This ArticleDo the sharing thingy

About AuthorMore info about author

Kamalika Some is an NCFM level 1 certified professional with previous professional stints at Axis Bank and ICICI Bank. An MBA (Finance) and PGP Analytics by Education, Kamalika is passionate to write about Analytics driving technological change.

See the rest here:

Cognitive AI and the Power of Intelligent Data Digitalization - Analytics Insight

A new technology developed by the Szeged Biological Research Center (SBRC) is using artificial intelligence (AI) to test for coronavirus in blood samples. The new method involves automatic microscope and AI technology; so far, it has been able to identify infections with almost 100% accuracy.

In collaboration with the University of Szeged, the University of Helsinki and Single-Cell Technologies Kft., the SBRC developed a new serological (blood) test to screen for the coronavirus.

The test identifies both currently infected and already recovered patients and calculates the degree of the patients immunity with exceptional accuracy. Thousands of tests already completed have been almost 100% accurate. The test has not yet produced any false positive results. The new technology enables the completion of five to ten thousand tests per day.

Coronavirus: Szeged Research Team Identifies New COVID-19 Receptor

The technology behind the test relies on the identification of the immunoglobulins produced by the patients own body. These proteins build up quickly after the contraction of the disease and they stay in the blood of those recovered for months. The test involves the addition of the blood sample to cells which are then studied by the AI which uses deep learning technology to train itself to detect the immunoglobulins more precisely.

The completion of the test requires six to eight hours. Furthermore, it is relatively cost effective and it is also capable of identifying the infection in cases of low levels of immune response.

Featured photo illustration by Gyrgy Varga/MTI.

Follow this link:

Researchers at Szeged Use AI to Screen for Coronavirus - Hungary Today

Watch all the Transform 2020 sessions on-demand right here.

When considering transformational ways to use computer vision on the edge in devices like robots, drones, cameras, and other devices, Booz Allen Hamilton VP Josh Sullivan advises caution, urging people to take security seriously on whats become a whole new attack vector.

For me, deploying an AI model in your IT environment is an entirely new attack vector. Ive seen a model working correctly that can identify tanks and other military equipment be fooled into seeing a school bus because someone sent poisoned data into the model, he said.

Failure to keep models secure can lead to adversarial machine learning attacks to make malicious code appear as benign or a range of other bad outcomes. Sullivan was joined in conversation at VentureBeats Transform 2020 conference by Nvidia VP of federal initiatives Anthony Robbins, Intel IoT VP Stacey Shulman, and Joint AI Center acting director Nand Mulchandani. Advances in encrypted models or use of federated learning may be required so that models are not as valuable if left in a war zone or discovered by a combatant. Booz Allen Hamilton signed a $800 million contract with the Pentagons Joint AI Center last month.

I think for the conversations were having like about facial recognition and law enforcement and privacy are crucial ones for us to have as a nation, but I also dont want to ignore the equally profound conversation around adversarial machine learning and the ability to hack AI models with poisoned data in attempts to influence their predictions, Sullivan said. Were going to keep relying more and more on these predictions and youre going to create an outsized impact that becomes possible by hijacking just a few bits of training data to change the entire outcome. I dont want to bolt the front door and leave the entire back door popped open.

The Joint AI Center was created by the Department of Defense in 2018 to lead Pentagon AI efforts. The initiative was led by Air Force Lt. Gen. Jack Shanahan until he retired and was replaced by Mulchandani, a civilian who said he sold four startups and spent 20 years in Silicon Valley before coming to the Pentagon one year ago. Mulchandani told VentureBeat in an interview last month how JAIC is evolving to adopt a culture and workflow more in line with how Silicon Valley treats software sprints and investment.

In a press conference at the Pentagon last week, Mulchandani talked about a global war for AI talent and said JAICs first lethal application of AI, what he describes as JAICs flagship product for joint warfighting operations, will be a tactical edge device.

Returning to the subject of tactical edge AI today, Mulchandani said advances in batteries, chip sets, algorithms, and other areas will be necessary to make progress in deploying AI at the edge. We are really relying on the private industry and breakthroughs and the technology platforms that private industry will enable that we can consume but also customize and deploy for our own use case, so its been a fascinating move, he said. That entire end to end process is an incredibly complicated one and is going to be. I think if somebody is thinking about starting a company or are partners on this webcast if theyre investing, which they are this is really where I think the game is going to get played out from an exciting standpoint in the next gen of technology here.

Each participant in the panel conversation has active contracts with the Department of Defense. Interest in government contracts may increase for some businesses as COVID-19 has introduced more volatility to the economy.

In addition to Mulchandanis appointment to acting director last month, other recent high-profile moves between the Pentagon and Silicon Valley include current White House CTO Michael Kratsios. He was chief of staff to Palantir founder and investor Peter Thiel at one time, but on Monday news emerged that he has been appointed Acting Under Secretary of Defense for Research and Engineering, according to his LinkedIn page.And going the other way, Josh Marcuse was executive director of the Defense Innovation Board in the office of the Secretary of Defense, but in March became head of strategy and innovation in Googles public sector division vying for government contracts.

Connections between Silicon Valley and the Pentagon appear to be increasing. Interaction has grown in part through the activity of groups like the Joint AI Center and the Defense Innovation Unit, a group created in 2015 with offices in Palo Alto.

For example, Google employees spoke out against the companys involvement in Project Maven, an initial JAIC project. A few months after the Maven contract came to light, Google adopted AI principles, but in a conversation with former JAIC director Air Force Lt. Gen. Jack Shanahan last fall, Google VP of global affairs Kent Walker reaffirmed the companys commitment to competing for government contracts.

The Defense Innovation Board is led by former Google CEO Eric Schmidt, while the National Security Council on AI, a group advising Congress on AI policy and investments, includes leaders from companies like AWS, Google Cloud, and Microsoft. Each of the groups advocates for more partnership between industry, academia, and government. Analysis by Tech Inquiry earlier this month found extensive government contracts between tech companies and the military.

Nvidias Anthony Robbins called public-private partnerships a giant team sport and identified opportunities for startups in cybersecurity, robotics, and other services. Running and retraining models at the edge is going to define the next decade, he said.

The rest is here:

Silicon Valley execs and Pentagon AI chief talk AI at the edge - VentureBeat

Physicist Max Tegmark wants to make artificial intelligence work for everyone. Here he waxes lyrical about cosmology, consciousness and why AI is like fire

By Max Tegmark

All possible universes exist, even triangular ones. These were the words on the cover of New Scientist on 6 June 1998, when Max Tegmark made one of his first appearances in the magazine. Inside, the then 31-year-old expanded on his idea of a multiverse on steroids, in which all logically possible universes not only can but must exist.

Tegmark, now a professor at the Massachusetts Institute of Technology (MIT), is known for his provocative ideas. As he explains in the Crazy section of his website: Every time Ive written ten mainstream papers, I allow myself to indulge in writing one wacky one. But the outlandish elements shouldnt overshadow his serious track record in cosmology, quantum information science and the study of some of the very deepest questions about the nature of reality.

Recently, Tegmark has shifted his focus to intelligence, both human and artificial. He conducts front-line research in artificial intelligence (AI), most recently working with fellow MIT researcher Silviu-Marian Udrescu to create an AI that was able to rediscover some of the most fundamental equations of physics by studying patterns in data. In 2014, he co-founded the Future of Life Institute, which aims to understand and mitigate existential risks to humanity, particularly those associated with the rise of AI.

Richard Webb: What made you switch from cosmology to working on artificial intelligence?

Max Tegmark: Ive always been fascinated by big questions, the bigger the better. Thats why I loved studying the universe, because there were philosophically very big questions like where does everything come from, whats going to happen, what is our place in the

Read more here:

Max Tegmark interview: "AI can be the best thing ever for humanity" - New Scientist

Computer scientists have launched an app, 'Bullstop' that uses novel artificial intelligence (AI) algorithms to combat trolling and bullying online.

The downloadable app is the only anti-cyberbullying app that integrates directly to social media platforms to protect users from bullies and trolls messaging them directly, the scientists from Aston University in the UK, reported.

Bullstop is available for free and can now be downloaded on GooglePlay.

"This application differs from other apps because the use of artificial intelligence to detect cyberbullying is unique in itself," said Semiu Salawu, who designed Bullstop.

"Other anti-cyberbullying apps, in comparison, use keywords to detect instances of bullying, inappropriate or threatening language," Salawu added.

According to the developer, the detection AI has been trained on over 60,000 tweets to recognise not only abusive and offensive language, but also the use of subtle means such as sarcasm and exclusion to bully, which are otherwise difficult to detect using keywords.

"It uses a distributed cloud-based architecture that makes it possible for 'classifiers' to be swapped in and out. Therefore, as better artificial intelligence algorithms become available, they can be easily integrated to improve the app," Salawu explained.

The team revealed that 'Bullstop' is unique in that it monitors a user's social media profile and scans for offensive incoming messages, to ensure the user is not subject to incoming abuse, as well as offensive outgoing messages.

This works via an algorithm which is designed to understand written languages. It analyses messages and flags offensive content, such as instances of cyberbullying, abusive, insulting or threatening language, pornography and spam.

Offensive messages can be immediately deleted from the user's inbox.

A copy of deleted messages are, however, retained should the user wish to review them. The app can also automatically block contacts who continuously send offensive messages.

Bullstop is highly configurable, allowing the user to determine how comprehensively the app removes inappropriate messages.

The app currently supports Twitter with support for text messages planned in the next stage of the rollout.

It is hoped that, with continued usage of the app and good results, other social media platforms such as Facebook and Instagram will come on board, allowing their users to benefit from the application.

The app is currently in the beta testing stage which means the researchers invite users of the app to provide them with feedback to allow them to make improvements.

"It has already been tested by a number of young people and professionals, including teachers, police officers and psychologists," the authors wrote.

Read the rest here:

Bullstop! This AI-based app to protect you from social media bullying - Zee Business

Verizon (NYSE:VZ) and IBM (NYSE:IBM) announced on Wednesday an extensive new partnership that would focus on a host of forward-looking technology, including 5G, edge computing, and artificial intelligence (AI).

The companies plan to use Verizon's high-speed, low-latency wireless 5G network, multi-access edge computing (MEC) capabilities, and Internet of Things (IoT) devices and sensors, and combine them with IBM's expertise in AI, hybrid multicloud, edge computing, asset management, and connected operations.

Image source: Getty Images.

By joining forces and leveraging each business's unique expertise, Verizon and IBM will initially offer mobile asset tracking and management solutions designed to help enterprises "improve operations, optimize production quality, and help clients enhance worker safety."

IBM and Verizon will also work to develop combined solutions for 5G and edge computing such as near-real-time cognitive automation for industrial applications. The combined solutions could help clients "detect, locate, diagnose and respond to system anomalies, monitor asset health and help predict failures in near real-time."

Many computation-intensive tasks happen at a data center, which can be thousands of miles from where the information is generated. Edge computing takes the processing of data from the cloud and moves it closer to the source, or the "edge."

The companies plan to use Verizon's lightning-fast 5G network to increase the number of IoT devices that can be used in a particular geographic area. This will give organizations the ability to interact with those devices in near real time by bringing the necessary computing power within close proximity of the devices.

Verizon and IBM hope to develop "innovative new applications" that could include remote control robotics, near-real-time cognitive video analysis, and industrial plant automation.

Visit link:

Verizon and IBM Will Partner on 5G and AI - The Motley Fool

Global AI in Supply Chain & Logistics Market to reach USD 10157.17 Million by 2025.Global AI in Supply Chain & Logistics Market valued approximately USD 502.9 Million in 2017 is anticipated to grow with a healthy growth rate of more than 45.60 % over the forecast period 2018-2025. Growth in this market is mainly driven by factors such as growing big data, demand for greater visibility and transparency into supply chain data and processes, and adoption of AI for improving consumer services and satisfaction. The major restraint for the market is the limited number of the artificial intelligence technology experts. But the limited number of artificial intelligence technology experts is expected to restrict adoption, which in turn may limit market growth to a certain extent. And The artificial intelligence in supply chain market for software offerings is expected to hold a larger share. The continuous developments have been witnessed in AI software and related software development kits

Request To Download Sample of This Strategic Report:https://www.kennethresearch.com/sample-request-10012727

The regional analysis of GlobalAI in Supply Chain & Logistics Marketis considered for the key regions such as Asia Pacific, North America, Europe, Latin America and Rest of the World. The artificial intelligence in supply chain market in APAC is expected to grow at the highest CAGR during the forecast period. Market growth can be attributed to increasing adoption of deep learning and NLP technologies for automotive, retail, and manufacturing applications in APAC. Moreover, the presence of major players in the artificial intelligence in supply chain ecosystem results in the increasing adoption of artificial intelligence in APAC.

The objective of the study is to define market sizes of different segments & countries in recent years and to forecast the values to the coming eight years. The report is designed to incorporate both qualitative and quantitative aspects of the industry within each of the regions and countries involved in the study. Furthermore, the report also caters the detailed information about the crucial aspects such as driving factors & challenges which will define the future growth of the market. Additionally, the report shall also incorporate available opportunities in micro markets for stakeholders to invest along with the detailed analysis of competitive landscape and product offerings of key players. The detailed segments and sub-segment of the market are explained below:

By Component:

*Hardware*Software*Services

By Technology:

*Machine Learning*NLP*Context Aware Computing*Computer Vision

By Regions:*North Americao U.S.o Canada*Europeo UKo Germany*Asia Pacifico Chinao Indiao Japan*Latin Americao Brazilo Mexico*Rest of the World

Furthermore, years considered for the study are as follows:

Historical year 2015, 2016Base year 2017Forecast period 2018 to 2025

The industry is seeming to be fairly competitive. Some of the leading market players include IBM, BonVision Technology, Intel, SAP, General Electric, Amazon Web Services (AWS), Google, LogiNext, Transmetrics, Smarter Sorting, Crowdz, Marble, Evertracker, Deliverish, Avrios International, GlobalTranz and so on. Acquisitions and effective mergers are some of the strategies adopted by the key manufacturers. New product launches and continuous technological innovations are the key strategies adopted by the major players.

Request To Download Sample of This Strategic Report:https://www.kennethresearch.com/sample-request-10012727

Target Audience of the Global AI In Supply Chain & Logistics Market in Market Study:

*Key Consulting Companies & Advisors*Large, medium-sized, and small enterprises*Venture capitalists*Value-Added Resellers (VARs)*Third-party knowledge providers*Investment bankers*Investors

About Kenneth Research

Kenneth Research is a reselling agency providing market research solutions in different verticals such as Automotive and Transportation, Chemicals and Materials, Healthcare, Food & Beverage and Consumer Packaged Goods, Semiconductors, Electronics & ICT, Packaging, and Others. Our portfolio includes set of market research insights such as market sizing and market forecasting, market share analysis and key positioning of the players (manufacturers, deals and distributors, etc), understanding the competitive landscape and their business at a ground level and many more. Our research experts deliver the offerings efficiently and effectively within a stipulated time. The market study provided by Kenneth Research helps the Industry veterans/investors to think and to act wisely in their overall strategy formulation

Contact Us

Kenneth ResearchEmail: [emailprotected]Phone: +1 313 462 0609Process Orchestration MarketGeographic Information System (GIS) MarketCloud Managed Services MarketBlockchain In Insurance MarketAIOps Platform MarketRouter and Switch MarketMobile Payment MarketIT Robotic Automation MarketEnterprise Application MarketData Center Cooling MarketBroadcast Switchers MarketVideo Management Software (VMS) MarketUnified Communication as a Service Market

Read more:

Artificial Intelligence (AI) in Supply Chain & Logistics Market : Business overview, Upcoming Trends and Top Company Analysis Forecast By 2025 -...