Monthly Archives: July 2017

In the race to get AI working faster on your smartphone, companies are trying all sorts of things. Some, like Microsoft and ARM, are designing new chips that are better suited to run neural networks. Others, like Facebook and Google, are working to reduce the computational demands of AI itself. But for chipmaker Qualcomm whose processors account for 40 percent of the mobile market the current plan is simpler: adapt the silicon thats already in place.

To this end the company has developed what it calls its Neural Processing Engine. This is a software development kit (or SDK) that helps developers optimize their apps to run AI applications on Qualcomms Snapdragon 600 and 800 series processors. That means that if youre building an app that uses AI for, say, image recognition, you can integrate Qualcomms SDK and it will run faster on phones with compatible processors.

Qualcomm first announced the Neural Processing Engine a year ago as part of its Zeroth platform (which has since been killed off as a brand). From last September its been working with a few partners on developing the SDK, and today its opening it up to be used by all.

Any developer big or small that has already invested in deep learning meaning they have access to data and trained AI models they are the target audience, Gary Brotman, Qualcomms head of AI and machine learning, told The Verge. Its simple to use. We abstract everything under the hood so you dont have to get your hands dirty.

The company says one of the first companies to integrate its SDK is Facebook, which is currently using it to speed up the augmented reality filters in its mobile app. By using the Neural Processing Engine, says Qualcomm, Facebooks filters load five times faster than compared to a generic CPU implementation.

How exactly developers will use the SDK will vary from job to job, but the basic task of the software is to allocate tasks to different parts of Qualcomms Snapdragon chipset. Depending on whether developers want to optimize for battery life or processing speed, for example, they can draw on compute resources from different parts of the chip eg, the CPU, GPU, or DST. It allows you choose your core of choice relative to the power performance profile you want for your user, explains Brotman.

The SDK works with some of the most popular frameworks for developing AI systems, including Caffe, Caffe2, and Googles TensorFlow. Qualcomm says its designed not just to optimize AI on mobile devices, but also in cars, drones, VR headsets, and smart home products.

what were seeing is a tidal wave of AI workloads.

But deploying frameworks that adapt existing silicon is only the beginning. What were seeing is a tidal wave of AI workloads that are creating more demand for compute, says Brotman. To meet this demand, companies are working on entirely new architectural designs for AI-optimized chips. Microsoft, for example, is building a custom machine learning processor for the Hololens 2, while British chipmaker Graphcore recently raised $30 million to build its own Intelligence Processing Units for mobile devices.

For Qualcomm, this switch is further down the line, but its definitely coming. When were baking something into silicon, thats a very deliberate bet for us, and it doesnt come easy, says Brotman. Computes compute, and if we can optimize now what weve already got in our portfolio then were doing our job well. Longer term, though, is there going to be a need for dedicated neural computing? I think thats going to be the case, and the question is just, when do we place that bet.

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Qualcomm opens up its AI optimization software, says dedicated mobile chips are coming - The Verge

HubSpot is announcing that it has acquiredKemvi, a startup applying artificial intelligence and machine learning to help sales teams.

A few months ago, Kemvi launched DeepGraph, a product that analyzes public data so that salespeople can identify the best time (say, after a job change or the publication of an article) to reach out to potential customers. It also proactively reaches out to verify leads.

Our vision has been to empower sales and marketing professionals by building technology that can extract information from text about whats happening in the world, said Kemvi founder and CEOVedant Misra.

And from the HubSpot perspective, Chief Strategy OfficerBrad Coffey said the company had been looking for new ways to bring AI technology into its platform. He acknowledged that AI and machine learning are buzzwords that get thrown around a lot right now, but he found Kemvi particularly appealing because it addressed a real need among salespeople.

What we want to do is focus on delivering tangible value to our customers, Coffey said. Thats what theyre here for theyre trying to understand the right way to grow their business and reach their customer. Its not that we want to invest in machine learning and AI for the academic interest of it.

The two-person Kemvi team, including Misra, will be joining HubSpot to work on bringing the startups technology into the HubSpot platform. Misra also said theres a transition plan for current Kemvi/DeepGraph customers: I think theyll be excited for what were working on at HubSpot.

The financial terms of the acquisition were not disclosed. Kemvi previously raised $1 million in funding from Seabed VC, Neotribe Ventures, Kepha Partners and others.

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HubSpot acquires Kemvi to bring more AI into its sales and marketing platform - TechCrunch

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

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

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