Category Archives: Ai

Tel Aviv-based startup Prospera has raised a $15 million Series B to expand the scope of its technology, which uses computer vision and artificial intelligence to help farmers analyze data gathered from their fields. The round was led by Qualcomm Ventures, with participation from Cisco Investments, ICV and returning investor Bessemer Venture Partners, and brings Prosperas total funding to $22 million (its Series A was covered by TechCrunch in July 2016).

The startups new capital will be used enter more global markets, add people to its delivery and customer-facing teams and broaden its services to cover more crops, including making a key shift from indoor farms to outdoor farms, which has huge implications given that 40 percent of U.S. land is farmland, says co-founder and CEO Daniel Koppel.

Since its Series A, Prospera has added new customers in Europe, Mexico, and the U.S. and now claims thousands of users, including produce growers for Walmart, Tesco, Sainburys, and Aldi.

Its technology has also evolved from its focus on automatically detecting pests and diseases to every aspect of farm production, says Koppel. This includes agronomy (the science of soil management and crop production), operations and managing a farms labor to increase its bottom line.

Some of the most notable companies also looking at agtech include drone makers like DJI and Agribotix (interestingly, Prosperas new investors Qualcomm and Cisco are both working tech to support the development and manufacturing of drones).

Koppel doesnt see those companies as competitors, but as potential partners. Drones will provide another valuable data stream for our analyses, enriching our database and potentially helping us provide even more value to our clients, he says.

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Agtech startup Prospera, which uses AI and computer vision to guide farmers, harvests $15M - TechCrunch

Executive Summary

Over the next few years, artificial intelligence is going to change the way we all gather information, make decisions and connect with stakeholders. Already, leaders are starting to use artificial intelligence to automate mundane tasks such as calendar maintenance and making phone calls. But AI can also help support decisions in key areas such as human resources, budgeting, marketing, capital allocation, and even corporate strategy long the bastion of bespoke consulting firms and major marketing agencies. According to recent research, the U.S. market for corporate advice alone is nearly $60 billion.Almost all that advice is high cost, human-based, and without the benefit of todays most advanced technologies. A great deal of what is paid for with consulting services is data analysis and presentation. They are very good at this, but AI may soon becomeeven better. Quant Consultants and Robo Advisers will soon offer faster, better, and more profound insights at a fraction of the cost and time of todays consulting firms and other specialized workers.

Amazons Alexa just got a new job. In addition to her other 15,000 skills like playing music and telling knock-knock jokes, she can now also answer economic questions for clients of the Swissglobal financial services company, UBS Group AG.

According to the Wall Street Journal (WSJ), a new partnership between UBS Wealth Management and Amazon allows some of UBSs European wealth-management clients to ask Alexa certain financial and economic questions. Alexa will then answer their queries with the information provided by UBSs chief investment office without even having to pick up the phone or visit a website. And this is likely just Alexas first step into offering business services. Soon she will probably be booking appointments, analyzing markets, maybe evenbuying and selling stocks. While the financial services industry has already begun the shift from active management to passive management, artificial intelligence will move the market even further, to management by smart machines, as in the case of Blackrock, which is rolling computer-driven algorithms and models into more traditional actively-managed funds.

But the financial services industry is just the beginning. Over the next few years, artificial intelligence mayexponentially change the way we all gather information, make decisions, and connect with stakeholders. Hopefully this will be for the better and we will all benefit from timely, comprehensive, and bias-free insights (given research that human beings are prone to a variety of cognitive biases). It will be particularly interesting to see how artificial intelligence affects the decisions of corporate leaders men and women who make the many decisions that affect our everyday lives as customers, employees, partners, and investors.

Already, leaders are starting to use artificial intelligence to automate mundane tasks such as calendar maintenance and making phone calls. But AI can also help support more complex decisions in key areas such as human resources, budgeting, marketing, capital allocation and even corporate strategy long the bastion of bespoke consulting firms such as McKinsey, Bain, and BCG, and the major marketing agencies.

The shift to AI solutions will be a tough pill to swallow for the corporate consulting industry. According to recent research, the U.S. market for corporate advice alone is nearly $60 billion. Almost all that advice is high cost and human-based.

One might argue that corporate clients prefer speaking to their strategy consultants to get high priced, custom-tailored advice that is based on small teams doing expensive and time-consuming work. And we agree that consultants provide insightful advice and guidance. However, a great deal of what is paid for with consulting services is data analysis and presentation. Consultants gather, clean, process, and interpret data from disparate parts of organizations. They are very good at this, but AI is even better. For example, the processing power of four smart consultants with excel spreadsheets is miniscule in comparison to a single smart computer using AI running for an hour, based on continuous, non-stop machine learning.

In todays big data world, AI and machine learning applications already analyze massive amounts of structured and unstructured data and produce insights in a fraction of the time and at a fraction of the cost of consultants in the financial markets. Moreover, machine learning algorithms are capable of building computer models that make sense of complex phenomena by detecting patterns and inferring rules from data a process that is very difficult for even the largest and smartest consulting teams. Perhaps sooner than we think, CEOs couldbe asking, Alexa, what is my product line profitability? or Which customers should I target, and how? rather than calling on elite consultants.

Another area in which leaders will soon be relying on AI is in managing their human capital. Despite the best efforts of many, mentorship, promotion, and compensation decisions are undeniably political. Study after study has shown that deep biases affect how groups like women and minorities are managed. For example, women in business are described in less positive terms than men and receive less helpful feedback. Minorities are less likely to be hired and are more likely to face bias from their managers. These inaccuracies and imbalances in the system only hurt organizations as leaders are less able to nurture the talent of their entire workforce and to appropriately recognize and reward performance. Artificial intelligence can help bring impartiality to these difficult decisions. For example, AI could determine if one group of employees is assessed, managed, or compensated differently. Just imagine: Alexa, does my organization have a gender pay gap? (Of course, AI can only be as unbiased as the data provided to the system.)

In addition, AI is already helping in the customer engagement and marketing arena. Its clear and well documented by the AI patent activities of the big five platforms Apple, Alphabet, Amazon, Facebook and Microsoft that they are using it to market and sell goods and services to us. But they are not alone. Recently, HBR documented how Harley-Davidson was using AI to determine what was working and what wasnt working across various marketing channels. They used this new skill to make resource allocation decisions to different marketing choices, thereby eliminating guesswork. It is only a matter of time until they and others ask, Alexa, where should I spend my marketing budget? to avoid the age-old adage, I know that half my marketing budget is effective, my only question is which half?

AI can also bring value to the budgeting and yearly capital allocation process. Even though markets change dramatically every year, products become obsolete and technology advances, and most businesses allocate their capital the same way year after year. Whether thats due to inertia, unconscious bias, or error, some business units rake in investments while others starve. Even when the management team has committed to a new digital initiative, it usually ends up with the scraps after the declining cash cows are fed. Artificial intelligence can help break through this budgeting black hole by tracking the return on investments by business unit, or by measuring how much is allocated to growing versus declining product lines. Business leaders may soon be asking, Alexa, what percentage of my budget is allocated differently from last year? and more complex questions.

Although many strategic leaders tout their keen intuition, hard work, and years of industry experience, much of this intuition is simply a deeper understanding of data that was historically difficult to gather and expensive to process. Not any longer. Artificial intelligence is rapidly closing this gap, and will soon be able to help human beings push past our processing capabilities and biases. These developments will change many jobs, for example, those of consultants, lawyers, and accountants, whose roles will evolve from analysis to judgement. Arguably, tomorrows elite consultants already sit on your wrist (Siri), on your kitchen counter (Alexa), or in your living room (Google Home).

The bottom line: corporate leaders, knowingly or not, are on the cusp of a major disruption in their sources of advice and information. Quant Consultants and Robo Advisers will offer faster, better, and more profound insights at a fraction of the cost and time of todays consulting firms and other specialized workers. It is likely only a matter of time until all leaders and management teams can askAlexa things like, Who is the biggest risk to me in our key market?, How should we allocate our capital to compete with Amazon? or How should I restructure my board?

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AI May Soon Replace Even the Most Elite Consultants - Harvard Business Review

Predictions of when machines will make us obsolete seem to come either from AI evangelists or doom-mongers with little practical experience of the field. Now though, researchers have carried out the largest-ever survey of machine learning experts on the subject.

The advent of AI that can outperform humans at various tasks will have a dramatic impact on society, so forecasting when particular skills or jobs will be automated could be invaluable for policymakers.

But the field is so fiendishly complex and has so many specialized sub-disciplines that there are very few people in a position to forecast when these breakthroughs will come. So instead, researchers at the Oxford Universitys Future of Humanity Institute decided to crowdsource the problem.

They contacted 1,634 researchers who published papers at the 2015 NIPS and ICML conferencesthe two leading machine learning conferencesand asked them to complete a survey on the topic, with 352 researchers responding.

When all the researchers answers were combined, the aggregate forecast was that there is a 50 percent chance that unaided machines can accomplish every task better and more cheaply than human workers within 45 years, and a 10 percent chance of it occurring within nine years.

Interestingly, there was a large discrepancy between the predictions of Asian respondents, who expect this to occur in 30 years, and North Americans, who expect it to take 74 years.

And when the question was worded slightly differently to gauge when all human labor would be automated rather than just when it could be, the aggregate forecast was a 50 percent chance in 122 years from now and a 10 percent chance within 20 years.

The survey also asked for predictions for when a few specific activities would be taken over by machines such as: translating languages (by 2024), writing high-school essays (by 2026), driving a truck (by 2027), working in retail (by 2031), writing a bestselling book (by 2049), and working as a surgeon (by 2053).

However, the usefulness of specific predictions like this is exemplified by the fact that back in 2015 the researchers predicted it would take until 2027 for an AI to beat a human at the board game Go. Google DeepMinds Alpha Go beat a top-ranked professional the following year and the worlds number one this year.

Perhaps more interesting are some of the broader findings of the survey, such as a perception that progress in machine learning is accelerating. More than two-thirds of respondents said progress was faster in the second half of their career and only 10 percent said progress was faster in the first half.

There was little support for one of the mainstays of AI evangelism, though. The intelligence explosion is the idea that once AI reach human-level intelligence, including in developing AI, their ability to operate in parallel and at far greater speeds than humans will lead to rapid growth in their capabilities.

When asked how likely it was that AI would perform vastly better than humans in all tasks two years after machines overtook human capabilities the median probability was just 10 percent. When asked whether there would be explosive global technological improvement after two years the median probability was 20 percent.

Unsurprisingly, the vast majority of respondents thought machines outperforming humans would have a positive impact on humanity. But 48 percent also said there should be more research aimed at minimizing the risks of AI.

While the results of the survey are informative, its important to remember that machine learning researchers are inherently enthusiastic about the technology. That means theyre liable to overestimate the speed of progress, while simultaneously underestimating the potential negative implications.

They are also probably not really qualified to judge how technological advances will interact with things like politics, economics, and human psychology. Just because a machine can do something doesnt necessarily mean it will. There are many other factors involved in determining whether AI will be widely adopted than just technological readiness.

Nevertheless, the perspective of those on the bleeding edge of AI research is an important one. While they may have blind spots, theyre certainly better positioned to pass judgment than many of the commentators weighing in on the debate. Lets just hope their optimism is well-founded.

Image Credit:Stock Media provided by maniaks3D/ Pond5

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When Will AI Be Better Than Humans at Everything? 352 AI Experts Answer - Singularity Hub

China's top administrative body has laid out a three-step approach to make artificial intelligence (AI) the key driving force of the country's economic growth for the next decade.

According to the plan initiated by the State Council and released last week, China will first keep pace with other leading countries in terms of AI technology and applications by 2020, aiming for a core AI industry worth 150 billion yuan ($22 billion) and AI-related fields worth 1 trillion yuan, according to a Tencent news report.

After the conclusion of the second phase by 2025 when legal grounds for the industry are established, the government plans to be the global leader in AI theory, technology, and applications and the major AI innovation centre globally by 2030. At which time, the core AI industry will value 1 trillion yuan and AI-related industries 10 trillion yuan, according to the blueprint.

The government has also pushed for vigorous development of AI-related emerging industries in China, including intelligent hardware and software, intelligent robots, and Internet of Things based devices.

Research on brain science, brain computing, quantum information and quantum computing, intelligent manufacturing, robotics, and big data will be greatly upheld, while intelligent upgrades in manufacture, agriculture, logistics, and home appliances will also be sped up.

A PwC report released last month has estimated the global GDP will become 14 percent higher in 2030 due to the wide deployment of AI.

"China will begin to pull ahead of the US's AI productivity gains in 10 years," the report said, and estimated that China will have the most economic gains from AI, which may boost China's GDP by 26 percent by 2030.

Chinese companies Alibaba, Baidu, and Lenovo are stepping up AI investment in a range of industries such as ecommerce, IoT, and autonomous driving.

Baidu announced the acquisition of Seattle-based startup Kitt.ai and a partnership with US chipmaker Nvidia this month, while Alibaba recently revealed an AI-powered smart speaker.

Lenovo also said AI will be a key feature of its products going forward, which include a digital assistant, connected health devices, and augmented and virtual reality platforms.

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China aims to become global AI leader by 2030 - ZDNet

Today, Microsoft announced that the next generation of its mixed reality HoloLens headset will incorporate an AI chip. This custom silicon a coprocessor designed but not manufactured by Microsoft will be used to analyze visual data directly on the device, saving time by not uploading it to the cloud. The result, says Microsoft, will be quicker performance on the HoloLens 2, while keeping the device as mobile as possible.

The announcement follows a trend among Silicon Valleys biggest tech companies, which are now scrambling to meet the computational demands of contemporary AI. Todays mobile devices, where AI is going to be used more frequently, simply arent built to handle these sorts of programs, and when theyre asked, the result is usually slower performance or a burned-out battery (or both).

But getting AI to run directly on devices like phones or AR headsets has a number of advantages. As Microsoft says, quicker performance is one of them, as devices dont have to upload data to remote servers. This also makes the devices more user-friendly, as they dont have to maintain a continuous internet connection. And, this sort of processing is more secure, as users data never leaves the device.

There are two main ways to facilitate this sort of on-device AI. The first is by building special lightweight neural networks that dont require as much processing power. (Both Facebook and Google are working on this.) The second is by creating custom AI processors, architectures, and software, which is what companies like ARM and Qualcomm are doing. Its rumored that Apple is also building its own AI processor for the iPhone a so-called Apple Neural Engine and now, Microsoft is doing the same for the HoloLens.

This race to build AI processors for mobile devices is running alongside work to create specialized AI chips for servers. Intel, Nvidia, Google, and Microsoft are all working on their own projects in this department. This sort of AI cloud power will service different needs to new mobile processors (itll primarily be sold directly to businesses), but from the viewpoint of designing silicon, the two goals are likely to be complementary.

Speaking to Bloomberg, Microsoft Research engineer Doug Burger said the company was taking the challenge of creating AI processors for servers very seriously, adding: Our aspiration is to be the number one AI cloud. Building out the HoloLens on-device AI capabilities could help with this goal, if only by focusing the companys expertise on chip architectures needed to handle neural networks.

For the second generation HoloLens, the AI coprocessor will be built into its Holographic Processing Unit or HPU Microsofts name for its central vision-processing chip. This handles data from all the devices on-board sensors, including the head-tracking unit and infrared cameras. The AI coprocessor will be used to analyze this data use deep neural networks, one of the principal tools of contemporary AI. Theres still no release date for the HoloLens 2, but its reportedly arriving in 2019. When it lands, AI will be even more central for everyday computing, and that specialized silicon will likely be in high demand.

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HoloLens 2 will have a custom AI chip designed by Microsoft - The Verge

When discussing the growing use of artificial intelligence, a hotly contested view is that AI will become a game-changer in healthcare diagnosing and treating patients with serious diseases, like cancer or diabetes.

While algorithm vs. doctor and clinical moonshots dominate the headlines, AI is quietly solving another big problem that has long plagued healthcare: waste and inefficiency.

Unlike clinical issues, inefficiency is often overlooked because its complicated and unsexy. However, many now believe that solving operational issues is the biggest lever for fixing healthcare and the area where we can really move the needle on cost and patient experience. This is more important than ever, as hospitals are seeing more bankruptcies and face growing uncertainties around reimbursements and operating margins in the face of ACHA and other turbulent policy issues.

But tackling inefficiency is hard. Thats because hospitals are complex, unpredictable organizations. Data was expected to help. But given the massive amount of information involved, standard industry tools like dashboards and reports arent useful enough. In healthcare, the stakes are much higher and require more practical solutions. How can we expect busy nurses and doctors to make sense of dashboards in high-pressure moments and figure out what decision to make? Its unreasonable and impossible.

We must also move away from rear window insights and into the proactive management of our problems. Lets say that a nurse reviews a report indicating that, the day before, a toddler had her surgery canceled due to lengthy delays in the OR. This insight is meaningless because its too late to fix the situation.

Many correctly believe that success requires predictive analytics, but predictions are hard to interpret. They cant help a busy nurse know exactly what she should do in that moment to prevent the chaos that may be heading her way. To make things work better for the frontline decision-makers, our tools have to be able to evaluate the possible interventions and suggest data-validated, real-time course corrections.

For example, in the situation of the toddler, AI can predict potential scheduling conflicts in the operating room, or flag when a delay is likely. It instantly identifies the best option and then prompts the nurse to take the specific action needed to prevent the cancellation. This way, better decisions are made and issues can be dealt with before they even arise.

The results associated with the use of AI in healthcare operations are compelling. During Beckers 8th Annual Symposium, a leading academic childrens hospital talked about a 25 percent reduction in same day surgery cancellations by using AI. Also, a prominent Midwest health system discussed steps for successfully transforming a low-performing emergency department reducing patient wait time for a doctor by 20 percent.

Using AI, our industry can extrapolate this success to avoid many issues currently affecting healthcare costs and patient experience such as surgery delays, overcrowding, patient falls and excessively lengthy hospital stays.

And, for our society, there are bigger goals that can be realized by focusing on these important and costly situations. Could we reduce painful facility closures facing our rural communities? Could we slow hospital spending that is now nearly $1 trillion dollars and represents a third of all healthcare costs in the U.S.? Could we reduce the 250,000 annual deaths from medical errors by making it easier for caregivers to do their job? On the most basic level, could we get patients in and out of the hospital faster and with less frustration?

To make an impact on these big picture outcomes, we have to change our mindset. Rather than seek out silver bullets, we must begin to value the small, day-to-day actions that, over time, can drive large-scale impacts.

Its crucial that we equip all healthcare staff with the best tools to do so. Thats where AI can be a game-changer the game; if we can mold the information it delivers into the right, actionable decisions. In the next few years it will be increasingly clear that those who are able to do so will see the greatest success.

Photo: Getty Images

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AI will fix healthcare's biggest and least sexy problem - MedCity News

Innovate UK is looking for as many ideas as possible for the application of machine learning to its existing data.

An increasing number of UK institutions and organizations are looking at artificial intelligence (AI) solutions that may help them enhance their work. The latest piece of proof in that respect, somewhat unsurprisingly, comes from Innovate UK, the organization that aims to propel growth by working with companies to de-risk, enable and support innovation.

Innovate UK, which has committed more than 1.8 billion to innovation since 2007, is inviting applicationsfor funding of projects involving machine learning. The aim of the proposals should be to improve the efficiency and effectiveness of Innovate UKs operational functions. Innovate UK is to invest up to 250,000 in such ideas.

The organization welcomes ideas for the application of machine learning to Innovate UKs existing data. The areas that may be covered include:

The competition opens today July 24, 2017. The registration deadline is set for September 6, 2017 Midday (12:00pm).

Earlier this month, the UK Financial Conduct Authority (FCA) has indicated its willingness to use regulatory technology (regtech) and AI solutions in orderto improve regulatory compliance. The FCA is examining the possibility of making its Handbook machine-readable and, then, fully machine-executable. This means that machines can interpret and implement the rules directly.

The variety of applications currently examined by the FCA range from the enhanced use of speech-to-text analytics tools within the FCA to solutions allowing better use of social media analytics. The regulator would also utilize AI to detect financial irregularities, according to Nick Cook, Head of Data and Information Operations at the FCA.

AI was one of the areas that dominated in the second cohort of the FCA regulatory sandbox, which allows firms to test innovative products and services in a live environment while making sure that consumers are appropriately protected.

The Bank of Englandis also welcoming AI solutions as shown by the third round of Proofs of Concept (POCs) completed by its FinTech Accelerator.

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Innovate UK seeks help from AI on improving efficiency of its operations - FinanceFeeds (blog)

Think about your favorite work of art. Why do you like it so much? What does it do for you?

Be it painting, sculpture, music, or writing, we love art not just for its beauty, but for the reactions and emotions it evokes in us. You probably feel a sort of kinship with your favorite artists even though youve never met them, because their work speaks to you in what feels like a unique and personal way.

How does this change when the art in question is produced by a machine and not a human? Is creativity an irreplaceable human skill, or will computers be able to learn it?

In a new video from Big Think, Andrew McAfee, associate director of MIT Sloan School of Managements Center for Digital Business, discusses these questions and explores the concept of creative AI.

McAfee notes that as it stands, AI can mimic some forms of creativity and create art. Generative design, for example, lets you input specifications including materials, budget, and manufacturing methods into software, and it generates design alternatives. In many cases these alternatives look and perform better than human-conceived designs.

Robots can paint in the style of a master artist or their own style. Software can also compose music, and when people dont know theyre listening to AI-generated music, they like it.

The standout feature of these computer-generated forms of art is that they require human inputs before theyre able to create something. Design software needs parameters to know what its working with, and music software needs code for the basic rules of music as a starting point.

Based on one of the definitions of creativity McAfee mentionsthe ability to come up with something thats valuable and also novelthis software technically qualifies as creative.

Luckily for us humans, though, McAfee offers a second, more profound definition of creativity: understanding the human condition, illuminating it, and reflecting it back to us in a way that we respond to.

While AIs can create original works of art if we give them some guidance, they certainly dont have any awareness of the fundamental conditions of being human, such as being aware of our own mortality, living inside a designated physical body, and probably most importantly, interacting with and relating to other humans.

McAfee calls our understanding of these the native speakers intuition about the human condition, and though hes a self-proclaimed technology optimist, he says, Im skeptical were going to be able to successfully convey that intuition even to a really big, really sophisticated piece of technology. If that day ever comes, its a long way away.

But besides wondering whether AI will ever be able to understand the human condition and reflect it back to us in a meaningful way, shouldnt we also be wondering whyor, better yet, whetherwe want it to be able to?

Weve already created AIs that can diagnose illness, drive cars, and win at Go. Siri can answer questions. Google Home and Amazon Echo help run our homes.

As more tasks become automatedand are thus performed far more efficiently than we were performing themmore jobs will be lost. Optimists believe the shift created by technological unemployment will unleash the worlds creativity, allowing us to work less and devote more time and energy to our true passionswhich for many people involve creative endeavors.

If this best-case scenario proves true and we end up with lots of time on our hands to create whatever our hearts desire, it seems like giving AI an understanding of the human condition would just be one more way to render ourselves obsoleteand in the process, relinquish the final quality that differentiates us from machines and makes us human.

Instead of asking whether AI can learn the one quality that makes humans creative, then, the more pertinent question is: should we let it?

The decision, for now, is in our (uniquely creative) hands.

Stock Media provided by bezikus / Pond5

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All Great Artists Share This One QualityCan AI Learn It Too? - Singularity Hub

Googles London-based AI outfit DeepMind has created two different types of AI that can use their imagination to plan ahead and perform tasks with a higher success rate than AIs without imagination. Sorry if I made you click because you wanted AIs predicted flying cars. I promise this is cool too.

In a post on their site, DeepMind researchers give a short review of a new family of approaches for imagination-based planning. The so-called Imagination-Augmented Agents, or I2As, use an internal imagination encoder that helps the AI decide what are and what arent useful predictions about its environment.

The researchers argue that giving AI imagination is crucial for dealing with real-world environments, where its helpful to test a few possible outcomes of actions in your head to predict which one is best.

Recently, DeepMinds founder Demis Hassabis wrote a paper published in Neuron about how the development of general-purpose AI is dependent on understanding and encoding human abilities like imagination, curiosity, and memory into AI. With these papers, his company seems to be making headway in at least one of those areas.

The I2A agents in the papers were tasked with different situations to test their predictive abilities, including the puzzle game Sokoban and a spaceship navigation game. Sokoban is a puzzle game in which a little alien has to push boxes into the right place it can not pull though, so one wrong move can screw up the whole round.

To challenge the agent, the researchers had every level procedurally generated and only gave the agent one try to solve it, because this encourages the agent to try different strategies in its head before testing them in the real environment, they wrote.

The agents ended up performing better than its imagination-less counterparts. They learned how to navigate the puzzles with less experience by extracting more information from their internal simulations. When the researchers added a manager component that helped create a plan, it learns to solve tasks even more efficiently with fewer steps.

Of course the type of imagination described in these papers is nowhere near what humans are capable of, but it does show that AIs can and benefit from being able to efficiently imagine different scenarios before acting.

As Hassabis wrote in the Neuron paper, creating agents with an imagination that can rival what we can do is perhaps the hardest challenge for AI research: to build an agent that can plan hierarchically, is truly creative, and can generate solutions to challenges that currently elude even the human mind. But step by step, we might be getting there.

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Google's DeepMind made an AI that can imagine the future - TNW

Imagine a world where A.I. is all around you. You get in your self-driving car to go to the doctors office, where a slew of tests are analyzed by machines that can diagnose your ailments with 99 percent accuracy. They give you a personalized prescription based on your individual biology, and then you go have a lunch of a cheeseburger and salad, one catering to your tastes and the other to your needs. Maybe you cheat and get fries with the cheeseburger, anyway.

For machines to accomplish that kind of work, they need the type of hardware that can handle the massive amount of data required. Thats where researchers from the Massachusetts Institute of Technology and Stanford University come in, as they recently developed a new type of three-dimensional chip made from different nanotechnologies that essentially puts the main two functions of chips under one roof. The chip streamlines the process and makes it easier for systems built from this chip to function as prescribed for A.I. systems.

Conventional chips basically come in two different flavors those for data storage, and those for processing, and they need to be linked in order to make the system run. In a paper published this month in the journal Nature, the research team outlines a new design for a chip that cobbles together both these functions.

The new chip is made of carbon nanotubes (sheets of 2D graphene morphed into nanocylinders) and resistive random-access memory (RRAM) cells, which charge the resistance of solid dielectric materials.

It might sound a bit complex, but what it basically means is that the RRAM and carbon nanotubes are stacked vertically over one another, creating a 3D architecture that lets a single chip fulfill multiple functions. This is beyond the capabilities of silicon-based chips.

Computers made with such a design could handle incredible amounts of bandwidth the type were likely going to need in complex computing structures that use A.I. and autonomous systems. Any machine learning applications would likely get a boost from a such a chip.

The technology could not only improve traditional computing, but it also opens up a whole new range of applications that we can target, said lead author Mark Shulaker in a statement. My students are now investigating how we can produce chips that do more than just computing.

The team is far away from demonstrating how the chip could be viably used in real world devices. But the fact that A.I. is still a work in progress gives the team plenty of time to figure out a sustainable way to manufacture and implement this chip in industrial and commercial applications.

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Scientists Create a 3D Chip Capable of Making AI Systems Work - Inverse