NEW DELHI: Online marketplace Snapdeal Wednesday announced an ‘AI Hackathon’ aimed at developing solutions based on the new-age technology that can help address challenges related to the e-commerce sector.

“This competition challenges participants to solve interesting problems around computer vision and natural language processing. The winner will be selected based on his/her ability to solve the real world business problems,” Snapdeal said in a statement.

Some of the key issues that could be taken include the ability to personalise search results to suit individual customer’s requirements and minimisation of fraud on e-commerce platforms.

As part of the 30-day competition, which begins September 10, the winner would have to beat the algorithm built by the internal team at Snapdeal as well as that built by other contestants.

Apart from a cash prize, the winner will also have an opportunity to join Snapdeal’s AI team.

Once a major player in the Indian online marketplace segment, Snapdeal was impacted severely by the intense competition in the e-commerce segment, led by Flipkart and Amazon. These two players alone have pumped in billions of dollars in investments to scale their operations in the country.
Last year, after its talks for a USD 950-million takeover by Flipkart fell through, Snapdeal founders Kunal Bahl and Rohit Bansal said the company will pursue a fresh strategy in the Indian market.
Dubbed as Snapdeal 2.0, the company has re-worked its business model and shed assets like Freecharge (sold to Axis Bank for Rs 385 crore) and logistics arm Vulcan Express (acquired by Kishore Biyani’s Future Supply Chain Solutions for Rs 35 crore).

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The field of artificial intelligence goes back a long way, but many consider it was officially born when a group of scientists at Dartmouth College got together for a summer, back in 1956. Computers had, over the last few decades, come on in incredible leaps and bounds; they could now perform calculations far faster than humans. Optimism, given the incredible progress that had been made, was rational. Genius computer scientist Alan Turing had already mooted the idea of thinking machines just a few years before. The scientists had a fairly simple idea: intelligence is, after all, just a mathematical process. The human brain was a type of machine. Pick apart that process, and you can make a machine simulate it.

The problem didn’t seem too hard: the Dartmouth scientists wrote, “We think that a significant advance can be made in one or more of these problems if a carefully selected group of scientists work on it together for a summer.” This research proposal, by the way, contains one of the earliest uses of the term artificial intelligence. They had a number of ideas—maybe simulating the human brain’s pattern of neurons could work and teaching machines the abstract rules of human language would be important.

The scientists were optimistic, and their efforts were rewarded. Before too long, they had computer programs that seemed to understand human language and could solve algebra problems. People were confidently predicting there would be a human-level intelligent machine built within, oh, let’s say, the next twenty years.

It’s fitting that the industry of predicting when we’d have human-level intelligent AI was born at around the same time as the AI industry itself. In fact, it goes all the way back to Turing’s first paper on “thinking machines,” where he predicted that the Turing Test—machines that could convince humans they were human—would be passed in 50 years, by 2000. Nowadays, of course, people are still predicting it will happen within the next 20 years, perhaps most famously Ray Kurzweil. There are so many different surveys of experts and analyses that you almost wonder if AI researchers aren’t tempted to come up with an auto reply: “I’ve already predicted what your question will be, and no, I can’t really predict that.”

The issue with trying to predict the exact date of human-level AI is that we don’t know how far is left to go. This is unlike Moore’s Law. Moore’s Law, the doubling of processing power roughly every couple of years, makes a very concrete prediction about a very specific phenomenon. We understand roughly how to get there—improved engineering of silicon wafers—and we know we’re not at the fundamental limits of our current approach (at least, not until you’re trying to work on chips at the atomic scale). You cannot say the same about artificial intelligence.

Common Mistakes

Stuart Armstrong’s survey looked for trends in these predictions. Specifically, there were two major cognitive biases he was looking for. The first was the idea that AI experts predict true AI will arrive (and make them immortal) conveniently just before they’d be due to die. This is the “Rapture of the Nerds” criticism people have leveled at Kurzweil—his predictions are motivated by fear of death, desire for immortality, and are fundamentally irrational. The ability to create a superintelligence is taken as an article of faith. There are also criticisms by people working in the AI field who know first-hand the frustrations and limitations of today’s AI.

The second was the idea that people always pick a time span of 15 to 20 years. That’s enough to convince people they’re working on something that could prove revolutionary very soon (people are less impressed by efforts that will lead to tangible results centuries down the line), but not enough for you to be embarrassingly proved wrong. Of the two, Armstrong found more evidence for the second one—people were perfectly happy to predict AI after they died, although most didn’t, but there was a clear bias towards “15–20 years from now” in predictions throughout history.

Measuring Progress

Armstrong points out that, if you want to assess the validity of a specific prediction, there are plenty of parameters you can look at. For example, the idea that human-level intelligence will be developed by simulating the human brain does at least give you a clear pathway that allows you to assess progress. Every time we get a more detailed map of the brain, or successfully simulate another part of it, we can tell that we are progressing towards this eventual goal, which will presumably end in human-level AI. We may not be 20 years away on that path, but at least you can scientifically evaluate the progress.

Compare this to those that say AI, or else consciousness, will “emerge” if a network is sufficiently complex, given enough processing power. This might be how we imagine human intelligence and consciousness emerged during evolution—although evolution had billions of years, not just decades. The issue with this is that we have no empirical evidence: we have never seen consciousness manifest itself out of a complex network. Not only do we not know if this is possible, we cannot know how far away we are from reaching this, as we can’t even measure progress along the way.

There is an immense difficulty in understanding which tasks are hard, which has continued from the birth of AI to the present day. Just look at that original research proposal, where understanding human language, randomness and creativity, and self-improvement are all mentioned in the same breath. We have great natural language processing, but do our computers understand what they’re processing? We have AI that can randomly vary to be “creative,” but is it creative? Exponential self-improvement of the kind the singularity often relies on seems far away.

We also struggle to understand what’s meant by intelligence. For example, AI experts consistently underestimated the ability of AI to play Go. Many thought, in 2015, it would take until 2027. In the end, it took two years, not twelve. But does that mean AI is any closer to being able to write the Great American Novel, say? Does it mean it’s any closer to conceptually understanding the world around it? Does it mean that it’s any closer to human-level intelligence? That’s not necessarily clear.

Not Human, But Smarter Than Humans

But perhaps we’ve been looking at the wrong problem. For example, the Turing test has not yet been passed in the sense that AI cannot convince people it’s human in conversation; but of course the calculating ability, and perhaps soon the ability to perform other tasks like pattern recognition and driving cars, far exceed human levels. As “weak” AI algorithms make more decisions, and Internet of Things evangelists and tech optimists seek to find more ways to feed more data into more algorithms, the impact on society from this “artificial intelligence” can only grow.

It may be that we don’t yet have the mechanism for human-level intelligence, but it’s also true that we don’t know how far we can go with the current generation of algorithms. Those scary surveys that state automation will disrupt society and change it in fundamental ways don’t rely on nearly as many assumptions about some nebulous superintelligence.

Then there are those that point out we should be worried about AI for other reasons. Just because we can’t say for sure if human-level AI will arrive this century, or never, it doesn’t mean we shouldn’t prepare for the possibility that the optimistic predictors could be correct. We need to ensure that human values are programmed into these algorithms, so that they understand the value of human life and can act in “moral, responsible” ways.

Phil Torres, at the Project for Future Human Flourishing, expressed it well in an interview with me. He points out that if we suddenly decided, as a society, that we had to solve the problem of morality—determine what was right and wrong and feed it into a machine—in the next twenty years…would we even be able to do it?

So, we should take predictions with a grain of salt. Remember, it turned out the problems the AI pioneers foresaw were far more complicated than they anticipated. The same could be true today. At the same time, we cannot be unprepared. We should understand the risks and take our precautions. When those scientists met in Dartmouth in 1956, they had no idea of the vast, foggy terrain before them. Sixty years later, we still don’t know how much further there is to go, or how far we can go. But we’re going somewhere.

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It’s tempting to believe that artificial intelligence can do just about anything.

Currently, AI and machine learning power self-driving cars, complex adtech audience optimizations and a host of intelligent agents like Alexa, Cortana and Siri. At the same time, leading AI experts, business owners and analysts are cautioning against an overly rosy view of its current capabilities.

Says Atrium co-founder and author Peter Kazanjy: “One of the biggest misunderstandings of AI and ML is the tendency to think of them as robots, bots — things we [fantasized about] growing up — like the robot housekeeper from The Jetsons [Rosie]. We’d assume that automation and AI would give us a robot like her that washes the dishes automatically — in addition to a lot of other tasks. But the dishwasher of the future really looks more like today’s dishwasher.”

The hype surrounding AI’s potential has misled many into overlooking its current utility. It will eventually redefine industries and build technologies we never thought possible, but business leaders and AI experts say the real value in today’s AI lies in increasing efficiency and accuracy in our everyday work.

The power of AI is in helping humans do things better.

The biggest reason to be bearish on AI is that it’s far from the sci-fi future we all dreamt of. AI today is narrow and focuses on doing classification tasks really well.

We’ll need to break down AI’s current capabilities to understand it better.

This is how the machines learn and the artificial becomes intelligent.

AI is defined as machines learning and making decisions in an intelligent, human-like way. This definition of AI is so broad — and unsatisfying — partially because the field itself is newer and less explored than the hype may lead you to believe.

The key to teaching computers is through machine learning, of which you’ve likely already heard. There are multiple ways to approach teaching computers to learn, and training often involves either supervised or unsupervised learning (supervised involves training with tagged data, while unsupervised does not).

Machine learning is the prevailing practical application of artificial intelligence, which works by finding patterns in data. (Note that deep learning, another promising and increasingly talked-about branch of AI, is referred to as the “cutting-edge of the cutting-edge” and is a subset of machine learning.)

The holy grail of AI — the kind that lives in Rosie’s CPU and can effortlessly think, act and talk like a human — is often referred to as “general AI” or “true AI.” But, depending on who you ask, it’s paradoxically both inevitable and implausible.

Says Microsoft Corporate Vice President Steve Guggenheimer: “We don’t think of AI in the context of an app, workload or process but instead as a transformative technology that will change how we do things in the future. When computers can draw conclusions from imperfect data, interpret meaning from text and interact with humans in more natural ways, it opens new possibilities than what existed before. AI today is already starting to make people more efficient at work and we are just scratching the surface of how AI can be used.”

What are AI’s current capabilities?

Great, you say. We’re millennia away from creating our own autobots and falling in love with our virtual assistants. What can artificial intelligence actually deliver?

Although it seems like second nature to us now, we’ve all had to learn how to use computers, download apps and memorize commands and shortcuts that power software applications.

Machine learning can simplify the learning curve. Its current capabilities can be sorted into four categories, according to Redpoint venture capitalist Tomasz Tunguz:

1. Optimization: calculating the shortest route to a destination.

2. Object identification: finding a particular thing within an image.

3. Anomaly detection: identifying data points that aren’t in line with established patterns (like credit card fraud detection).

4. Segmentation: learning to treat different groups of data differently (showing different display ads to millennials and baby boomers).

Additionally, computers can now generate security protocols, music and images.

These applications aren’t redefining the way we work, but they are accelerating workflows by reducing time spent on various tasks like data entry, language translation, scanning credit card charges for fraud and personally identifying objects in images one by one.

What’s more, they’re aiming to solve specific business problems, often tailored to individual industries. AI’s presence is not about reinventing the wheel and replacing you with a computer, according to SalesTing founder Piyush Saggi, but rather about addressing concrete use cases that make work more meaningful.

“Most knowledge workers are overworked because of mundane tasks; AI has the potential to help people get so much more out of their working hours,” he says.

In the context of B2B sales and marketing, AI is already being used to help sellers and marketers predict opportunities and fine-tune campaigns that would have been difficult to foresee otherwise. Marketers are using sentiment analysis to learn more about how their brands are being perceived and used. For example, Bing Search has the ability to perform object recognition within images so visitors can learn more about a product they have seen and where they can purchase it.

As Microsoft exemplified by creating Cortana, “computing is ambient, accessible, and everywhere around us. To draw from it, we need a guide—a smart conversationalist who can, in plain written or spoken form, help us navigate this new super-powered existence.”

Even though the AI of tomorrow may not include the futuristic robots we’ve imagined, its true value will be in simplifying our lives and processes.

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Talk of automation in manufacturing tends to focus on industrial robots, for obvious reasons.

The robotics market is growing at an unprecedented pace and vague worries about job loss due to automation—however misguided—often take shape in visions of robots replacing individual workers on production lines.

However, there’s a much less tangible form of automation that’s poised to make an even bigger impact on manufacturing in the near future: artificial intelligence (AI).

The concept is notoriously difficult to pin down. “I would say it’s as difficult to define as intelligence itself,” noted Philippe Beaudoin, SVP research at Element AI. But for manufacturers, what matters most is what AI can do.

The technology is already seeing applications in construction and additive manufacturing, as well as self-driving vehicles and industrial robotics. Broader applications for AI, ones that could be particularly useful in manufacturing, include analyzing large datasets and predictive maintenance.

Of course, most manufacturers aren’t interested in becoming AI experts, which is where Element AI comes in. Offering “AI as a Service”, the company provides applications that allow users to leverage artificial intelligence to tackle a variety of challenges, from speech recognition to automated decision making.

Element AI primarily works with clients that already have large data sets—such as the shipping schedules for busy ports or production data from factories—rather than gathering that data independently. This lets the company focus on building AI that can, for example, interpret high-frequency time series from distributed sensors on a production line to make maintenance recommendations.

As an added benefit, this allows Element AI to improve its systems with each new project the company takes on. That approach seems to be paying off: almost a year ago, the Montreal-based start up had eight employees. Today, after over $100 million USD in series A funding from the likes of Intel, Microsoft and NVIDIA, Element AI has a staff of 160.

There’s clearly a lot of enthusiasm here, but one might worry about the impact something as extensive as AI could have on manufacturing jobs. Beaudoin emphasized the cooperative role employees can play, particularly when it comes to potential bias in the data sets his company is using.

“Having a human in the loop is really important,” he said. “You can’t hide behind the fact that it’s an algorithm, because the machine is just capturing patterns in the data. So, you need to know your data.”

This raises questions regarding the ethical implications of making decisions based on machine learning. We’re not talking about hackneyed, Terminator-type scenarios, but more realistic concerns about, for example, the potential discriminatory aspects of automated decision-making. There’s even an industry organization that was created to deal with these issues.

For his part, Beaudoin expressed concern about this problem, but was careful to point out that its scope extends well beyond his own company. “The ethical problems are real, but they’d be out there with or without Element AI,” he said.

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