Monthly Archives: July 2017

While there are many trending and debate-worthy topics within the world of artificial intelligence, theres a rather profound one that few people are starting to discuss openly. Namely, theres a fundamental disconnect between how the tech industry communicates about innovations in AI versus the actual value delivered to consumers and enterprises. Worse, AI is not fully democratized and is not the bastion of major tech companies. Fortunately, that is about to change.

As consumers, we are awash in a myriad of daily stories concerning AI and machine learning, from IBM Watsons latest use case to the warnings of Stephen Hawking to the rise of AI-style terminators. The average user is perhaps vaguely aware that AI powers everything from their inbox to their music playlists to their social media feeds. Savvier users may be more familiar with AIs potential to impact industries on a global scale, such as in health care, advertising, finance, security, and more.

The impression we have, therefore, is that AI is widespread and easily accessible that humanity is benefiting from these groundbreaking applications, and that they impact our lives in a meaningful and helpful way. But this is a big misconception. The reality is that we have enormous strides to make in democratizing AI development, and making these innovations truly accessible and available to mankind.

As it stands now, the vast majority of AI is being developed within the enormous black hole of a few major technology platform companies. These household name tech giants are monopolizing the best and brightest human capital, and they have access to Big Data and other critical resources which is limiting the ability of other major global enterprises, let alone small to mid-size companies, to compete. These industry giants have their own specific business models and requirements, and as a result, they tend to focus on a relatively limited subset of AI applications. The problems they are tackling, while very real and worthwhile, are still just a tiny portion of AIs potential to impact specific industry vertical use cases and the overall economy, not to mention humanity as a whole.

A few tech industry titans thus control the vast majority of talent, data, and other resources necessary to develop life-changing technologies, and this is bad for any number of stakeholders who stand to benefit from AI. Competition should happen at the application and business levels, and not be based on a single industry monopoly.

The good news is that weve reached a tipping point, and AI is actually helping to shift the dynamics and level the playing field. As our systems become more advanced and the costs to develop new AI software begin their predictable fall, its becoming easier and easier for startups and smaller companies to rise up against the tech giants. Rather than focusing on a confined set of problems, these up-and-coming players will be free to cook up innovative, disruptive solutions that arent restricted by existing business models and product services.

Consider the infamous Innovators Dilemma as demonstrated by Clayton Christensen. If youre not familiar, the idea is that successful companies (so-called incumbents) can do everything right and by the books, yet theyll still lose their market leadership to new and rising competitors. There are two key parts to this dilemma. One is that the value to innovation is an S-curve, meaning that product improvement necessarily takes time and involves multiple iterations. By finding the right application and market, startups are able to find the sweet spot of value using iteration at a much faster rate, and thus enter and disrupt the more mature markets of the incumbents.

The second is the idea of incumbent-sized deals which means, while incumbents may have the advantage of a huge customer base, this carries higher expectations for yearly sales and performance. Startups dont need to worry as much about these requirements, and thus have more time and energy to focus on innovating a new entry, next-gen product.

AI has so many applications beyond the business needs of a few of black-hole tech platforms. Weve reached an exciting time when emerging technologies are facilitating smarter, faster, and better businesses processes at increasingly lower costs, and this is opening up the playing field to smaller, leaner players. It will become more and more common to see five-person startups go up against the tech behemoths. Top AI talent that has been incubated inside these companies will inevitably start to leave and create their own startups, addressing new use cases that had been ignored by their past employers. As more newcomers and startups make progress in AI development, we will surely witness a broader spectrum of adoption and, thus, a much greater and meaningful impact on society.

Roger Jin is the cofounder and CEO ofRul.ai,focusing on AI technologies.

Above: The Machine Intelligence Landscape This article is part of our Artificial Intelligence series. You can download a high-resolution version of the landscape featuring 288 companies by clicking the image.

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The dangers of letting Big Tech control AI - VentureBeat

In BriefScientists have created an artificially intelligent systemthat is capable of producing cutting edge paintings that someconsider to be better than works created by humans. How do thepaintings, and other AI creations, relate to seminal criticisms ofmodern art? An AI Picasso

Scientists are using artificial intelligence (AI)to find a new system for generating art and testing their results on the public. The system, called a generative adversarial network (GAN), works by pairing two AI neural networks:a generator, which produces images, and a discriminator, which judges the paintings. It does this based on the 81,500 example paintings and knowledge of different artistic styles (such as Baroque, Impressionism, and Modernism) it was taught. The suggester creates an image, the discriminator criticizes it, and the conversation leads to a work of art.

The scientists changed the way that AI usually produces art by having the generatoronly create works that did not fall into a preexistent category of painting they did this by maximizing deviation from established styles and minimizing deviation from art distribution, according to the abstract.

Mark Riedl, an associate professor at the Georgia Institute of Technology in Atlanta,said that he liked the idea that people are starting to push GANs out of their comfort zone this is the first paper Ive seen that does that.

After the paintings were produced, the scientists conducted a survey with members of the public in which they mixed the AI works with paintings produced by human artists. They found that the public preferred the works by AI, and thought they were more novel, complex, and inspiring.

Paul Valry, who Walter Benjamin used as a starting point for his essay The Work of Art in the Age of Mechanical Reproduction, wrote in 1931: We must expect great innovations to transform the entire technique of the arts, thereby affecting artistic invention itself and perhaps even bringing about an amazing change in our very notion of art.

He was referring to the modernist period, in which new techniques and ideologies changed the way art was perceived. We may be experiencing a similar upheaval in the art world. Benjamins criticism of the exact copies that could be produced by the second half of the 20th century centered around the idea that even the most perfect reproduction of a work of art is lacking in one element: its presence in time and space, its unique existence at the place where it happens to be.

This AI project possesses this property. It does not just copy or manipulate, as Google Deep Dream does, but is able to producetrue works of art by being actively programmed to be novel and creating originals in a specific place. These pieces are more similar to Aiva, an AI composerthat also could not be detected by humans, than it is to Deep Dream.

We are entering an age where AI is becoming increasingly ubiquitous and competent in almost every fieldElon Musk thinks it will exceed humans at everything in by 2030 but art has been viewed as a pantheon of humanity, something quintessentially human that an AI could never replicate.

Studies such as this show that our artistic leanings may not be off limits and with AI conquering humans at our own games, like chess how long is it before we create a Picasso program that is superior to any current human artist and immortal to boot?

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AI Can Now Produce Better Art Than Humans. Here's How. - Futurism

If youre not doing AI today, dont expect to be around in a few years, says Japjit Tulsi, VP of engineering at eBay,It really is that important for companies to invest in especially commerce companies.

Tulsi will speak next week at MB 2017, July 11 and 12 in SF, MobileBeats flagship event where this year weve gathered more than 30 brands to talk about how AI is being applied in businesses today.

eBay is working to stay ahead of the curve, now that machine learning and AI is growing in importance. It has focused on the potential of AI for the past ten years. The companys approach to AI has been built on a platform of research and development, Tulsi says, plus decades of insights and data about consumer behavior, making even the simplest applications incredibly valuable.

As an example, Tulsi points to the merchandizing strip at the bottom of every item page, which shows similar items that a shopper might be intrigued by, and often leads them down a positive rabbit hole of shopping and buying.

Itsmachine learning and AI at the very simplest level, and weve seen a tremendous amount of return on investment on that. Tulsi says.

However, evolving that into more sophisticated personalization has proven difficult, say Tulsi, because of the limitations on computing power in the past 10 years. Then in 2015 or so, processors hit the event horizon, with game-changing advances in GPUs and the dedicated hardware used for deep learning.

Massive calculations can now be made swiftly and cost-effectively. New algorithms are increasing the speed and depth of learning. And deep learning can now go broad across billions of data points with thousands of aspects and dozens of layers.

eBay has no shortage of data. The company manages about 1 billion live listings and 164 million active buyers daily, and receives 10 million new listings via mobile every week.

So another big bet was born: Investment in AI technologies like natural language understanding, computer vision, and semantic search, to drive growth and, Tulsi says, reinvent the future of commerce.

The future looks pretty much like their engineering team building descriptive and predictive models from the enormous volume of behavioral and description data generated by eBays many buyers, sellers, and products. It requires the complex fusion of massive amounts of behavior log, text, and image data, all with a particular emphasis on on developing data-driven models to improve user experience.

The question now is, can we provide you with even further personalized, relevant information over the course of the next ten years? he says. Were very focused on how AI will impact commerce.

Specifically, how it will impact the primary goal of commerce: understanding consumer buying intent wherever they are, from bricks and mortar to online browsing. Of course, cross-platform understanding of what a shopper wants is the key to delivering a truly personal, contextual shopping experience.

You want an exact item that youre looking for whether you want it, you need it, or you just like it at the price point you care about, Tulsi says. With AI, our aim is to achieve that kind of perfection underneath the hood so you dont have to spend a lot of time finding that ideal match for you.

He points at one of their beta projects, launched last year on Facebook Messenger: the eBay ShopBot. Its essentially a multimodal search engine, or a personalized shopping assistant, powered by contextual understanding, predictive modeling, and machine learning.

Keywords are not enough any more, and dont offer the most optimized shopping experience. With ShopBot, consumers can text, talk, or snap a picture, and then the assistant asks questions to better understand your intent and dig up hyper-personalized recommendations. And it gets smarter about what you want, every time you use it.

These consumer interactions also yield a tremendous amount of intent data, which can be poured right back into the algorithm.

Across the three spectrums of multimodal AI that it represents, were starting to get much much better at understanding you and whichever way that you want to interact with us, Tulsi says.

And as theyre able to improve their ability to simulate human cognitive capabilities like perception, language processing. and visual processing, the company expects that commerce will become increasingly conversational even to the point where the search box becomes redundant.

What I think is really exciting going forward is the machine will actually do the thinking for you, Tulsi laughs. You will just talk naturally to it as if youre talking to a friend and spitballing and the machine should be able to understand your intent.

And just as importantly, commerce will will become present wherever and whenever the user is engaged on their social messaging platforms.

Its an approach that digital assistant-focused companies should sit up and take notice of, Tulsi adds. They need to start investing in commerce capabilities or partnering with commerce companies to really make their assistant pan out from a financial model perspective.

From our perspective, every company should be heavily investing in AI, and it shouldnt just be about using cognitive services but actually developing your own models that keep you on the cutting edge of technology, Tulsi says. And that will hold you in good stead over the course of the next many years to come.

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For eBay, AI is ride or die - VentureBeat