Category Archives: Ai

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Now that AI is heading into the mainstream of IT architecture, the race is on to ensure that it remains secure when exposed to sources of data that are beyond the enterprises control. From the data center to the cloud to the edge, AI will have to contend with a wide variety of vulnerabilities and an increasingly complex array of threats, nearly all of which will be driven by AI itself.

Meanwhile, the stakes will be increasingly high, given that AI is likely to provide the backbone of our healthcare, transportation, finance, and other sectors that are crucial to support our modern way of life. So before organizations start to push AI into these distributed architectures too deeply, it might help to pause for a moment to ensure that it can be adequately protected.

In a recent interview with VentureBeat, IBM chief AI officer Seth Dobrin noted that building trust and transparency into the entire AI data chain is crucial if the enterprise hopes to derive maximum value from its investment. Unlike traditional architectures that can merely be shut down or robbed of data when compromised by viruses and malware, the danger to AI is much greater because it can be taught to retrain itself from the data it receives from an endpoint.

The endpoint is a REST API collecting data, Dobrin said. We need to protect AI from poisoning. We have to make sure AI endpoints are secure and continuously monitored, not just for performance but for bias.

To do this, Dobrin said IBM is working on establishing adversarial robustness at the system level of platforms like Watson. By implementing AI models that interrogate other AI models to explain their decision-making processes, and then correct those models if they deviate from norms, the enterprise will be able to maintain security postures at the speed of todays fast-paced digital economy. But this requires a shift in thinking away from hunting and thwarting nefarious code to monitoring and managing AIs reaction to what appears to be ordinary data.

Already, reports are starting to circulate on the many ingenious ways in which data is being manipulated to fool AI into altering its code in harmful ways. Jim Dempsey, lecturer at the UC Berkeley Law School and a senior advisor to the Stanford Cyber Policy Center, says it is possible to create audio that sounds like speech to ML algorithms but not to humans. Image recognition systems and deep neural networks can be led astray with perturbations that are imperceptible to the human eye, sometimes just by shifting a single pixel. Furthermore, these attacks can be launched even if the perpetrator has no access to the model itself or the data used to train it.

To counter this, the enterprise must focus on two things. First, says Dell Technologies global CTO John Roese, it must devote more resources to preventing and responding to attacks. Most organizations are adept at detecting threats using AI-driven event information-management services or a managed-security service provider, but prevention and response are still too slow to provide adequate mitigation of a serious breach.

This leads to the second change the enterprise must implement, says Rapid7 CEO Corey Thomas: empower prevention and response with more AI. This is a tough pill to swallow for most organizations because it essentially gives AI leeway to make changes to the data environment. But Thomas says there are ways to do this that allow AI to function on the aspects of security it is most adept at handling while reserving key capabilities to human operators.

In the end, it comes down to trust. AI is the new kid in the office right now, so it shouldnt have the keys to the vault. But over time, as it proves its worth in entry-level settings, it should earn trust just like any other employee. This means rewarding it when it performs well, teaching it to do better when it fails, and always making sure it has adequate resources and the proper data to ensure that it understands the right thing to do and the right way to do it.

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Can you trust AI to protect AI? - VentureBeat

A key selling point for emerging fintech is the potential to expand financial access to more people -- but there is a potential for biases built into the technology to do the opposite.

The rise of online lenders, digital-first de novo banks, digital currency, and decentralized finance speaks to a desire for greater flexibility and participation in the money-driven world. While it might be possible to use such resources to better serve unbanked and underbanked segments of the population, how the underlying tech is encoded and structured might cut off or impair access for certain demographics.

Sergio Suarez Jr., CEO and founder of TackleAI, says when machine learning or AI is deployed to look for patterns and there is a history of marginalizing certain people, the marginalization effectively becomes data. TackleAI is a developer of an AI platform for detecting critical information in unstructured data and documents. If the AI is learning from historical data and historically, weve been not so fair to certain groups, thats what the AI is going to learn, he says. Not only learn it but reinforce itself.

Fintech has the potential to improve efficiency and democratization of economic access. Machine learning models, for example, have sped up the lending industry, shortening days and weeks down to seconds to figure out mortgages or interest rates, Suarez says. The issue, he says, is that certain demographics have historically been charged higher interest rates even if they met same criteria as another group. Those biases will continue, Suarez says, as the AI repeats such decisions.

Essentially the technology regurgitates the biases that people have held because that is what the data shows. For example, AI might detect names of specific ethnicities and then use that to categorize and assign unfavorable attributes to such names. This might influence credit scores or eligibility for loans and credit. When my wife and I got married, she went from a very Polish last name to a Mexican last name, Suarez says. Three months later, her credit score was 12 points lower. He says credit score companies have not revealed how precisely the scores were calculated, but the only material change was a new last name.

Structural factors with legacy code can also be an issue, Suarez says. For instance, code from the 1980s and early 1990s tended to treat hyphenations, apostrophes, or accent marks as foreign characters, he says, which gummed up the works. That can be problematic when AI built around such code tries to deal with people or institutions that have non-English names. If its looking at historical data its really neglecting years, sometimes decades worth of information, because it will try to sanitize the data before it goes into these models, Suarez says. Part of the temptation process is to get rid of things that look like garbage or difficult things to recognize.

An essential factor in dealing with possible bias in AI is to acknowledge that there are segments of the population that have been denied certain access for years, he says, and make access truly equal. We cant just continue to do the same things that weve been doing because well reinforce the same behavior that weve had for decades, Suarez says.

More often than not, he says, developers of algorithms and other elements that drive machine learning and AI do not plan in advance to ensure their code does not repeat historical biases. Mostly you have to write patches later.

Amazon, for example, had a now-scrapped AI recruiting tool that Suarez says gave much higher preference to men in hiring because historically the company hired more men despite women applying for the same jobs. That bias was patched and resolved, he says, but other concerns remain. These machine learning models -- no one really knows what theyre doing.

That brings into question how AI in fintech might decide loan interest rates are higher or lower for individuals. It finds its own patterns and it would take us way too much processing power to unravel why its coming to those conclusions, Suarez says.

Institutional patterns can also disparagingly affect people with limited income, he says, with fees for low balances and overdrafts. People who were poor end up staying poor, Suarez says. If we have machine learning algorithms mimic what weve been doing that will continue forward. He says machine learning models in fintech should be given rules ahead of time such as not using an individuals race as a data point for setting loan rates.

Organizations may want to be more cognizant of these issues in fintech, yet shortsighted practices in assembling developers to work on the matter can stymie such attempts. The teams that are being put together to work on these machine learning algorithms need to be diverse, Suarez says. If were going to be building algorithms and machine learning models that reflect an entire population, then we should have the people building it also represent the population.

Fintechs Future Through the Eyes of CES

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DC Fintech Week Tackles Financial Inclusivity

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How Fighting AI Bias Can Make Fintech Even More Inclusive - InformationWeek

Narrow AI vs. General AI- Whats Next for the Future of Tech?

Artificial Narrow Intelligence (ANI) or Narrow AI, also known as Weak AI describes artificial intelligence systems that are specified to handle a singular or limited task.

Narrow AI is programmed to perform a single task like playing a particular game, analyzing data to create a report, checking the weather, etc.

Narrow AI acts similar to a computer system and displays a certain degree of intelligence in a particular field. It performs highly specialized tasks for humans, within that narrow field.

Some of the Narrow AI features that people use in their daily life includes self-driving cars, facial recognition tools, chatbots, spam filters, etc.

Artificial General Intelligence (AGI) or General AI, also known as Strong AI describes a certain mindset of AI development that aims to create intelligent machines which are indistinguishable from the human mind.

General AI is capable of performing any intellectual tasks that a human being can do. General AI acts similar to humans and copes with any generalized tasks which are asked of it.

General AI is where the technology sector is headed to. To reach general AI, computer hardware needs to increase in computational power to perform more total calculations per second (cps).

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Narrow AI vs. General AI- What's Next for the Future of Tech? - Analytics Insight

Since the beginning of this year, there has been a lot of hype, skepticism, cynicism, and confusion surrounding the concept of the metaverse.

For some, it has added to the confusion of an already elusive world of augmented reality and mixed reality. But for the well-initiated, the metaverse is a landmark moment in the extended reality world; a world approaching the second life that many have long predicted.

News that some of the worlds top tech firms are rapidly developing AI supercomputers has further fueled that anticipation.

But what will the entry of supercomputers mean for the metaverse and virtual reality and how can we manage it responsibly?

Simply put, a supercomputer is a computer with a very high level of performance. That performance, which far outclasses any consumer laptop or desktop PC available on the shelves, can, among other things, be used to process vast quantities of data and draw key insights from it. These computers are massive parallel arrangements of computers or processing units which can perform the most complex computing operations.

Whenever you hear about supercomputers, youre likely to hear the term FLOPS floating point operations per second. FLOPS is a key measure of performance for these top-end processors.

Floating numbers, in essence, are those with decimal points, including very long ones. These decimal numbers are key when processing large quantities of data or carrying out complex operations on a computer, and this is where FLOPS comes in as a measurement. It tells us how a computer will perform when managing these complicated calculations.

The supercomputer market is expected to grow at a compound annual growth rate of about 9.5% from 2021 to 2026. Increasing adoption of cloud computing and cloud technologies will fuel this growth, as will the need for systems that can ingest larger datasets to train and operate AI.

The industry has been booming in recent years, with landmark achievements helping to build public interest, and companies all over the world are now striving to outcompete and outpace the competition on their own supercomputer projects.

In 2008, IBMs Roadrunner was the first to break the one petaflop barrier meaning it could process one quadrillion operations per second. According to one study, the Fugaku supercomputer, based in the RIKEN Centre for Computational Science in Kobe, Japan, is the worlds fastest machine. It is capable of processing 442 petaflops per second.

In late January, Meta announced on social media that it would be developing an AI supercomputer. If Metas prediction is true it will one day be the worlds fastest supercomputer.

Its sole purpose? Running the next generation of AI algorithms.

The first phase of its creation is already complete, and by the end of 2022 the second phase is expected to be finished. At that point, Metas supercomputer will contain some 16,000 total GPUs, and the company has promised that it will be able to train AI systems with more than a trillion parameters on data sets as large as an exabyte or one thousand petabytes.

While these numbers are impressive, what does this mean for the future of AI?

Meta has promised a host of revolutionary uses of its supercomputer, from ultrafast gaming to instant and seamless translation of mind-bendingly large quantities of text, images and videos at once think about a group of people simultaneously speaking different languages, and being able to communicate seamlessly. It could also be used to scan huge quantities of images or videos for harmful content, or identify one face within a huge crowd of people.

The computer will also be key in developing next-generation AI models, it will power the Metaverse, and it will be a foundation upon which future metaverse technologies can rely.

But the implications of all this power mean that there are serious ethical considerations for the use of Metas supercomputer, and for supercomputers more generally.

The World Economic Forums Centre for the Fourth Industrial Revolution, in partnership with the UK government, has developed guidelines for more ethical and efficient government procurement of artificial intelligence (AI) technology. Governments across Europe, Latin America and the Middle East are piloting these guidelines to improve their AI procurement processes.

Our guidelines not only serve as a handy reference tool for governments looking to adopt AI technology, but also set baseline standards for effective, responsible public procurement and deployment of AI standards that can be eventually adopted by industries.

We invite organizations that are interested in the future of AI and machine learning to get involved in this initiative. Read more about our impact.

New technologies have always demanded societal conversations about how they should be used and how they should not. Supercomputers are no different in this regard.

While AI has been brilliant at solving some large and complex problems in the world, there still remain some flaws. These flaws are not caused by the AI algorithms instead, they are a direct result of the data that is fed into the AI systems.

If the data fed into systems has a bias, then the result of an AI calculation is bound to carry that bias and, if the metaverse and virtual reality do become a second life, then are we bound to carry with us the flaws, prejudices and biases of the first life?

The age of AI also brings with it key questions about human privacy and the privacy of our thoughts.

To address these concerns, we must seriously examine our interaction with AI. When we look at the ethical structures of AI, we must ensure its usage is transparent, explainable, bias-free, and accountable.

We must be able to explain why a certain calculation or process was initiated in the first place, what exactly happened when the AI ran it, make sure there was no initial human bias against any group or idea, and be clear about who should be held accountable for the results of a calculation.

It remains to be seen whether these supercomputers and the companies producing them will ensure that these four key areas are consistently and transparently addressed. But it will become all the more pressing as they continue to wield more power and influence over our lives both online and in the real world.

The surge in the supercomputing era will push the era of parallel computing and use cases at the speed of thought. We see a future where a combination of supercomputers and intelligent software will run on a hybrid cloud, feeding partial workflows of computation to a quantum computer, a form of computing that experts believe has the capacity to exceed even that of the fastest supercomputers.

What remains to be seen is how this era will fuel the next generation of metaverse experiences.

Written by

Arunima Sarkar, Lead, Artificial Intelligence and Machine Learning, World Economic Forum

Nikhil Malhotra, Chief Innovation Officer, Tech Mahindra

The views expressed in this article are those of the author alone and not the World Economic Forum.

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What you should know about the metaverse, AI and supercomputers - World Economic Forum

Technology that farmers have only dreamed about will be on full display next week at Tulare's annual World Ag Expo.

New robotics, artificial intelligence and zero-emission vehicles look to steal the three-day show the largest of its kind at a time when problems like water and labor scarcity are giving inventors plenty to work on.

Advancements in computer software and hardware are being brought to bear on those and other challenges, either through new diagnostics and analysis or equipment built to help move crops more quickly or more cleanly.

Mechanization hasn't evolved to the point where Kevin Andrew, senior vice president of Bakersfield-based farming company Illume Ag, can stop hiring people to do the manual work of growing and harvesting grapes. He said even top international designers he has met with "sort of glaze over" when he explains the tasks involved.

Still, he said technologies that were only talked about five, 10 years ago, such as the latest AI and mechanization, "have kind of become front and center right now."

Andrew said he's particularly encouraged that many different companies have entered the race to come up with the best machines and that existing manufacturers keep introducing upgrades.

"The more companies that come into it," he said, "we'll have a better chance of getting something out of it."

The expo, which starts Tuesday at the International Agri-Center and ends Thursday, has named a top-10 products list that trends heavily toward computer technology.

Half the items on the list are actual robots. One by Nao Technologies emits no pollution as it uses artificial intelligence and automation on large-scale vegetable crops, reducing the need for herbicides as it collects useful data.

There's a "people-scaled collaborative robot" by Burro that works alongside farmworkers with the use of GPS and sensor equipment, and an autonomous sprayer by GUSS Automation LLC that's now more compact than earlier versions.

A robot by InsightTRAC rolls through orchards targeting pests and taking down data, while a dairy automation tool by Onfarm Solutions uses a gantry system to spray cows teats before and after milking.

Also on the top-10 list was an all-electric refrigeration truck by Hummingbird EV and an electric tractor by Solectrac. A mobile data-management tool by TJ Hoof Care made the ranking, as did software by Tule Technologies that helps withirrigation decisions. The other product on the list is a clip plug by Rain Bird for better water management.

Technology will also be a prominent topic on the expo's seminar lineup. On opening day alone there will be a talk on the prosperous use of artificial intelligence and a discussion of ransomware.

On Wednesday, a discussion is scheduled on the "bumpy road" from high-tech ideas to practical field equipment. The final day's seminars are on ag manufacturing and the future of electric vehicles, the "rise of autonomous machine functions" in agriculture and new technologies for improving food security.

Kern County grower John C. Moore III said he's one of those expo attendees who goes mainly to "attend the breakfasts, reconnect with colleagues and marvel at the new equipment," adding that he rarely, if ever, buys new equipment within six months of the event.

But he won't be surprised if some of his peers in local ag take great interest in some of the new innovations.

"Automation is No. 1 for everyone with overtime and minimum wage increases, absent any meaningful increases to most commodity prices," he said by text.

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Ag Expo rolls out innovations in AI, robotics, electric vehicles - The Bakersfield Californian

Singapore has released a software toolkit aimed at helping financial institutions ensure they are using artificial intelligence (AI) responsibly. Five whitepapers also have been issued to guide them on assessing their deployment based on predefined principles.

The Monetary Authority of Singapore (MAS) said the documents detailed methodologies for incorporating the FEAT principles--of Fairness, Ethics, Accountability, and Transparency--into the use of AI within the financial services sector.

The whitepapers were developed by the Veritas consortium, which is part ofSingapore's national AI strategyand comprises 27 industry players that include Amazon Web Services, Bank of China, Bank of Singapore, Google Cloud, Goldman Sachs, OCBC Bank, and Unionbank of the Philippines.

According to MAS, the whitepapers provide a FEAT checklist to guide financial institutions in their AI and data analytics software development lifecycles as well as an enhanced fairness assessment methodology to define the objectives of their AI and data analytics systems and identify potential bias.

There also is a methodology for assessing ethics and accountability, which offers a framework to help financial institutions carry out quantifiable measurement of ethical practices, and another for assessing transparency so these organisations can determine how much internal and external transparency is needed to explain and interpret predictions generated by machine learning models.

The Veritas consortium also developed the software toolkit to automate the fairness metrics assessment and facilitate visualisation of the assessment interface. Available on GitHub, the open source toolkit allows for plugins to enable integration with the financial institution's IT systems.

MAS' chief fintech officer Sopnendu Mohanty said in the statement released Friday: "The new open source software, assessment methodologies, and enhanced guidance will further improve the technical capabilities of financial institutions in developing responsible AI for the financial sector."

Some members of the Veritas consortium also applied the methodologies to various functions within their organisation, including customer marketing, insurance fraud detection, and credit risk scoring.

The group next would develop additional use cases and conduct pilots with selected financial institutions within the consortium to further integrate the methodologies with their existing governance framework.

MAS added that it was working with Infocomm Media Development Authority and Personal Data Protection Commission (PDPC) to include the tolkit in the PDPC's Trustworthy AI testing framework.

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Singapore releases software toolkit to guide financial sector on AI ethics - ZDNet

Combining automation and augmented intelligence to propel data-driven action is among the top analytics trends in 2022.

That was the outlook of some analysts speaking at MicroStrategy World 2022, the virtual user conference hosted by longtime independent analytics vendor MicroStrategy.

Analysts Matt Aslett of Ventana Research, Mike Gualtieri of Forrester Research, Carsten Bange of BARC (Business Application Research Center) and Ray Wang of Constellation Research identified the trends they predict will drive analytics in the coming year.

A key trend over the last few years that analysts said they expect to continue this year is enterprises increasingly using AI technologies in concert with analytics.

But rather than merely adopt AI capabilities such as natural language processing (NLP) and automated machine learning (AutoML) -- which many vendors have been developing in recent years -- this year is expected to be when organizations actually operationalize AI capabilities to take data-driven action.

That means deploying capabilities that combine AI capabilities with process automation.

"Organizations know that they're competing for data supremacy," Wang said. "It's about going from data to decisions and winning on data velocity. We make a decision per second, but it takes a week, a month, a year for decisions to get out of a management committee.

"Machines are making a hundred or a thousand decisions per second, and that real-time input -- that real-time insight -- is important," he continued.

Wang added that to get that real-time insight, organizations need to automate processes such as data capture and data management while using AI and machine learning to capture and contextualize events like interactions between employees and customers or suppliers and partners.

"We see analytics AI and automation powering the future," Wang said. "Ask the right business questions, automate the data capture, automate the next best action -- a suggestion -- and use AI to build the back end. That is powerful. That is building the long-term future."

Similarly, Gualtieri said he sees the combined use of AI and automation as an analytics trend this year.

We see analytics AI and automation powering the future. Ask the right business questions, automate the data capture, automate the next best action -- a suggestion -- and use AI to build the back end. That is powerful. That is building the long-term future. Ray WangAnalyst, Constellation Research

And enterprises are employing that synergy between the technologies not only to enable business users to more easily interact with and analyze data to come up with insights, but also to deliver automatically generated insights directly to those business usersat the moment they need them.

Analytics vendors have been developing and enhancing NLP and AutoML capabilities for years, but enterprises have been relatively slow to adopt them.

In a 2021 survey conducted by Dresner Advisory Services, only about a quarter of respondents said they were using natural language capabilities to enable analytics. Natural language analytics ranked 32nd out of 41 business intelligence-related technologies.

Meanwhile, a report from Prescient & Strategic Intelligence showed the global AutoML market is a fraction of what it will be in 2030, with a market size of $346.2 million as of 2020 and expected size of $14.8 billion in 2030.

NLP and AutoML reduce barriers to analytics by eliminating the need to know and write code, but they still force end users to explore data, train models and develop their own insights.

Process automation combined with AI, however, delivers insights.

MicroStrategy, with an aim of enabling intelligence everywhere, is one vendor focused on delivering insights. Qlik, whose strategy is to enable active intelligence, is another.

"There's going to be continued use of AI and automation," Gualtieri said. "Now, where will AI be used in automation? Within decisions. I'm looking forward to accelerated use of AI, and for transforming digital business processes."

Regarding the timing of combining automation and AI being an analytics trend, Bange said 2022 will be the year because organizations have been experimenting with the capabilities and they're ready to put into action what they've developed.

"The change for many companies is they're saying, 'Playtime is over,'" he said. "They experimented a lot, tried pilots, but now it's about operationalizing it, bringing it into production to generate real business value. That is a huge challenge, and an underrated change."

Beyond combining automation and AI to generate insights, a few underrated analytics trends also will be important in 2022.

In particular, the emphasis on data quality and data governance.

The two concepts and their enabling technologies are not as groundbreaking as automation and AI, but they're equally important. In fact, without good data quality and a strong data governance framework, insights generated with automation and AI could be completely untrustworthy.

"Nobody wants to talk about data quality, but it's still the number one problem for everyone working with data," Bange said. "That time is running out, and maybe in 2022 or next year is the last chance for many companies to get data quality right. If they don't get the foundation right, they will start to lose their competitiveness."

Data governance is closely tied to data quality in forming a foundation for analytics, and their importance remains a significant analytics trend, according to Aslett.

"Data governance is an enabler of improved and accelerated data analytics," he said. "Data was previously under-governed, and better data governance can be the foundation for delivering the right data to the right people at the right time and actually accelerate projects moving forward."

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Use of AI and automation together an analytics trend in '22 - TechTarget

In 2016, Apple announced that it had developed a recycling robot, called Liam, that could deconstruct an iPhone in 11 seconds. Six years and several machine generations later, Apple still wont disclose how many iPhones its robots have recycled for parts.

But the potential impact of artificially intelligent robots on e-waste recycling more broadly might soon become clear, thanks to a new research project that seeks to develop AI-powered tools that allow a robotic recycler to harvest parts from many different models of phones. If such technology can be commercialized, researchers are hopeful it could vastly improve the recycling of smartphones and other small, portable electronics.

While todays e-waste recyclers are mostly handling larger legacy devices like CRT TVs, a growing number of smaller electronics like smartphones and tablets have started to reach their facilities. This creates new challenges, as these devices are often difficult and time-consuming to take apart. Instead of salvaging potentially valuable components like the motherboard, recyclers typically remove the battery and shred the rest. Precious materials are lost in the process, and all of the energy that went into manufacturing components needs to be expended again to create new ones.

For several years, scientists have been exploring whether artificially intelligent robots could streamline the recycling process, making the recovery and reuse of parts from dead consumer electronics more economical. In December, the idea received a high-level boost when the US Department of Energy awarded a $445,000 grant to researchers from Idaho National Laboratory, the University of Buffalo, Iowa State University, and e-waste recycler Sunnking to develop software that allows robots to automatically identify different types of smartphones on a recycling line, remove the batteries, and harvest various high-value components. By the end of the two-year research project, the team hopes to field-test an early version of its technology at one of Sunnkings facilities after which it may pursue additional funding to commercialize robotic smartphone recyclers.

Amanda LaGrange, CEO of the St. Paul-based e-waste recycler TechDump, says that the work these researchers are doing is critical for improving the sustainability of consumer electronics, which contain valuable metals and minerals that todays crude recycling processes dont recover. Finding ways, like these scientists are with robots, of trying to reclaim rare earth metals is so important, LaGrange tells The Verge. Also, my jaded self is not convinced it can be done at scale at this point.

Indeed, applying robotics and AI to e-waste recycling is a fairly new idea, and there arent a lot of practical examples of it working. The best-known example is Apples much-hyped line of recycling robots, but only a few versions of these robots are out in the wild, they only work on iPhones, and their impact on Apples overall e-waste remains murky at best. A jack-of-all-trades version that could be installed at an e-waste facility processing dozens of different models of smartphones has not been commercialized yet. The new research project aims to show that such a robot is, at least, possible to develop.

Various research teams will take the lead on different robotic recycling capabilities. Researchers at INL will focus on developing methods for removing batteries from smartphones using a robotic arm. In parallel, researchers at the University of Buffalo and Iowa State University will identify higher-value components, like circuit boards, cameras, and magnets, that can be removed from dead phones using the same robots and find or develop hardware to do the actual smartphone surgery.

The robots dont just need good hardware, but software that allows them to quickly recognize different phone types and look up their internal anatomy. For this part of the project, Iowa State University researchers and Sunnking will be developing a database that includes 2D images and 3D scanning data on various makes and models of smartphones. Using a machine learning approach, that database will train the software guiding the robots to locate the phones battery and high-value components and extract them.

Were going to train that system to look at phones and say, This is an iPhone, this is a Samsung model XYZ, then go to a database and say, This is where were going to cut the battery out, says INLs Neal Yancey, the principal investigator on the project.

Eventually, the researchers hope to have a smartphone-stripping robot that can be plugged into existing e-waste recycling operations. Sunnking, which will be providing 100 samples of five different phone models for the researchers to experiment with, will be the first to test that system out toward the end of the two-year project window.

At the same time, researchers at INL will analyze the economics of the entire robotic disassembly process to determine if it actually reduces recycling costs. The teams goal is to improve materials recovery by at least 10 percent and recycling economics by at least 15 percent compared with standard recycling operations today.

Even those seemingly modest goals may be difficult to achieve. Adding specialized robotic arms to e-waste operations where phones are currently taken apart by hand will require a potentially sizable up-front investment. (The cost of robotic arms can vary widely, but the popular UR5 series sell for upwards of $35,000 apiece.) And with most of todays robots designed for simple, repetitive tasks rather than the precision work of removing tiny phone parts, developing a robot that can measure up to its human counterparts in terms of disassembly speed and accuracy is no small feat, says Minghui Zheng, a roboticist at the University of Buffalo and co-principal investigator on the project.

There are lots of limitations of robots, Zheng says. Basic tasks, like using robotic grippers to pull out small components, could be very challenging, she says.

Developing AI-based software tools that can sift through the complex mixture of dead devices in an e-waste stream and accurately classify them could also prove challenging, although similar tools exist for sorting through solid wastes like plastic. Other groups are also attempting to develop AI-based e-waste sorting methods, including Carnegie Mellon Universitys Biorobotics Lab, which recently worked with Apple on one such project.

Even if the initial research is promising, more work will be needed before AI-powered robots are a practical solution for handling the estimated 150,000 tons of portable consumer electronic waste Americans produce each year (a figure including not just smartphones but tablets and wearables like Apple Watch). With the initial project focused on just five of the hundreds of smartphones out there, the tech will need to be developed further to be practical for most recyclers. To process large volumes of smartphones in an industrial setting, the system will also need to be scaled up.

Product design changes could create another barrier to robotic recycling. As companies tweak their devices year after year, recycling robots will need to be kept up to date with hardware and software capable of handling the latest models. An e-waste recycler thats considering investing in such technology might reasonably worry that in 10 years, new phone designs will have rendered the robots obsolete.

Thats why its so important that recyclability is baked into product design, says Sara Behdad, a sustainable electronics researcher at the University of Florida whos not involved with the new research project. While Behdad says that greater use of robots could improve e-waste recycling a lot, she believes that many of the issues plaguing recyclers today, from glued-in batteries to proprietary screws, should be addressed through design for disassembly standards.

Such an approach would mean less uncertainty for recyclers in the future, Behdad says. And taking phones apart would be much more within the capabilities of robots.

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An AI-powered robot could one day recycle your smartphone - The Verge

Where did you find inspiration for the pieces made for Mixmag?

Its really an exploration of beauty more than anything. In this particular context there is a combination of objects, textures and shapes that I see in the world that I like. Ill notice how the light falls on a particular texture or material and I will essentially command, talk, or even ask the AI nicely to collate all this stuff together. Sometimes you get interesting results, but a lot of the skill in the process is learning how to talk to this oblique code and getting the results that you want out of it. In terms of the actual images having meanings, I think that the images are more like aesthetic figuration. But the process itself is quite interesting as its another conversational collaboration, not only with the coders who are building the code, but also the model, which is all the images of how they are training the code on a machine learning process. Theres quite a lot of nice meaning behind it, as its me making these things sitting down at the computer, but in essence collaborating with potentially 1000s of people as part of the process, which I love.

There are ways to make it do quite specific things by giving it an existing image, and you can do all kinds of sculpting to guide it, but I like the process where I use a specific combination of words which throw a tonne of imagery. Youll come up with prompts and see if the AI can do much with them, and if it cant do much with those words you think of others which seem to open up whole worlds. And thats my thing. I somehow found a way to talk to it which seems to be a little different to how other people are talking to it. Its just conjuring up all this really beautiful imagery. Its amazing to start putting it out there and get in touch with old friends like Teneil and make new friends with this aesthetic world gaining a sense of community and collaboration.

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How do you know HAAi and how did you work with her to execute the vision?

Weve been friends for a long time. I probably met her before her sort of ascendancy. But everyone I know, knows HAAi. And everyones just super stoked for everything thats been happening with her and to see her career take off. I was very honoured and happy to get asked to get on board and work with her, and the process was super easy which is exactly what you want. She had no complaints and she just loved everything that came forward.

Ive done a lot of freelance music work over the year and often its quite an arduous process, but with Mixmag it was a very smooth. I was just able to do my thing which is very nice and very satisfying.

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How the first Mixmag cover animated by AI technology was made - Mixmag

A massive naval exercise underway in the Middle East is incorporating unmanned vehicles and artificial intelligence to test out ways to improve maritime awareness across a large geographic area.

These [14 training scenarios] were developed over the planning process to offer participating forces the opportunity to demonstrate the tremendous potential for unmanned systems and artificial intelligence to solve some of the complex problems we have here in the maritime environment, Cmdr. Tom McAndrew, the lead planner for Task Force X, which oversees the unmanned elements of the exercise, told reporters Wednesday.

This year, Fifth Fleet combined its International Maritime Exercise 2022 with 6th Fleets Cutlass Express exercise to increase cooperation between the fleets and develop relationships between other participants. The 18-day naval exercise involves 9,000 people and 50 ships from 60 nations, according to the Navy. Its operations are taking place in the Arabian Gulf, the Arabian Sea, the Gulf of Oman, the Red Sea, and the North Indian Sea.

The exercise also involves 80 air, surface, and underwater unmanned systems from the United States and nine other nations, said McAndrew. Task Force X consists of personnel from the U.S. Navys Task Force 59, which was stood up in September to work on quickly learn and integrating unmanned systems and artificial intelligence into the fleet.

Among the U.S. systems are the Saildrone Explorer, the Mantas T-12, and the Switchblade 300. These are controlled locally and underway from several maritime operations centers, and from a robotics operations center, McAndrews said.

Their operators are running them through 14 training scenarios designed to reveal how unmanned systems and AI can be used in real-world operations in the region. Navy leaders hope unmanned systems will help them keep better tabs on the goings-ons in the waters around the Arabian Peninsula, as well as above and below them

In simple terms, it's a big area of responsibility, and unmanned systems can help us improve our ability to detect, rapidly respond, and provide deterrence, McAndrews said.

Some of the scenarios they are looking to exercise with drones include how they can help to quickly find sailors who fall overboard or provide remote 24/7 monitoring of an area.

Some of the platforms we have, like if it's an airplane, has [sic] fuel requirements and a certain amount of time that it can stay on station. And many of these unmanned platforms are designed with that in mind. They can operate for days, weeks or months without intervention, McAndrews said.

One of the problems with monitor a vast region is sorting through the correspondingly vast amounts of photos, video, and data. Artificial intelligence promises to make it easier to spot and share useful information.

Ultimately, the whole goal is to help AI improve the commander's decision, McAndrews said.

Even countries that didnt bring an unmanned system can get involved in at least some of the scenarios in this unclassified exercise, Cmdr. Kenyatta Martin, the exercises lead planner. In the future, that classification could change as relationships grow and the ability to share data gets better.

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Navy Puts AI, Unmanned Systems to the Test in Five-Sea, 60-Nation Exercise - Defense One