Apply now to be an Entrepreneur 360 company. Let us tell the world your success story. Computer-based artificial intelligence (AI) has been around since the 1940s, but the current innovation boom around everything from virtual personal assistants and visual search engines to real-time translation and driverless cars has led to new milestones in the field. And ever since IBM's Deep Blue beat Russian chess champion Garry Kasparov in 1997, machine versus human milestones inevitably bring up the question of whether or not AI can do things better than humans (it's the the inevitable fear around Ray Kurzweil's singularity). As image recognition experiments have shown, computers can easily and accurately identify hundreds of breeds of cats and dogs faster and more accurately than humans, but does that mean that machines are better than us at recognizing what's in a picture?

A revolution in AI is occurring thanks to progress in deep learning. How far are we towards the goal of achieving human-level AI? What are some of the main challenges ahead? Yoshua Bengio believes that understanding the basics of AI is within every citizen's reach. That democratizing these issues is important so that our societies can make the best collective decisions regarding the major changes AI will bring, thus making these changes beneficial and advantageous for all.

There are three main ingredients to creating artificial intelligence: hardware (compute and memory), software (or algorithms), and data. We've heard a lot of late about deep learning algorithms that are achieving superhuman level performance in various tasks, but what if we changed the hardware? Firstly, we can optimise CPU's which are based on the von Neumann architectures that we have been using since the invention of the computer in the 1940's. These include memory improvements, more processors on a chip (a GPU of the type found in a cell phone, might have almost 200 cores), FPGA's and ASIC's. Such is the case with research being done at MIT and Stanford.

The days of claiming artificial intelligence as a feature that set one startup or company apart from the others are over. These days, one would be hard-pressed to find any technology company attracting venture funding or partnerships that doesn't posit to use some form of machine learning. But for companies trying to innovate in healthcare using artificial intelligence, the stakes are considerably higher, meaning the hype surrounding the buzzword can be deflated far more quickly than in some other industry, where a mistaken algorithm doesn't mean the difference between life and death. Over the past five years, the number of digital health companies employing some form of artificial intelligence has dramatically increased. CB Insights tracked 100 AI-focused healthcare companies just this year, and noted 50 had raised their first equity rounds since January 2015.

Most of today's artificial intelligence (AI) systems rely on machine learning algorithms that can predict specific outcomes by drawing on pre-established values, but now researchers from OpenAI, a company funded by no less than Elon Musk and Peter Thiel, who are trying to democratise AI for "human good" just discovered – literally – that a machine learning system they created to predict the next character in the text of reviews from Amazon evolved into an unsupervised learning system that could learn how to read sentiment. That's a pretty big deal, and it's also something that, at the moment, even the researchers themselves can't explain. "We were very surprised that our model learned an interpretable feature, and that simply predicting the next character in Amazon reviews resulted in discovering the concept of sentiment," said OpenAI in a blog. According to the post OpenAI's neural network was able to train itself and analyse sentiment accurately by classifying Amazon's reviews as either positive or negative – and it then generated follow on text that fit with the sentiment. The AI the team used was what's known as a multiplicative long short-term memory (LSTM) model that was trained for a month, processing 12,500 characters a second using Nvidia Pascal GPU's – which Nvidia's own CEO gifted to Elon Musk last year – with "4,096 units on a corpus of 82 million Amazon reviews to predict the next character in a chunk of text."

In this exploration notebook, we shall try to uncover the basic information about the dataset which will help us build our models / features. The Dataset of this competition is an anonymized sample of over 3,000,000 grocery orders from more than 200,000 Instacart users. Now we have to predict which previously purchased products will be in a user's next order.

The rise of a technology has Bill Gates issuing warnings of an apocalypse. It's Artificial Intelligence or AI, an idea whose time has come--it is incubating in science labs and being deployed by start-ups and industrial units alike. Why are Gates and Co. worried? Specifically, it's over machine learning, an early form of AI that has in recent years become mainstream, causing both delight and nervousness among AI experts and technology companies. AI involves building computers capable of taking smart decisions by themselves, the way humans do. Machine learning and various other sub-fields such as deep learning are the means to achieve AI. Google announced this week that it is rethinking all its products to base them on AI; it has created a new unit called Google.ai to facilitate this shift. The revival of interest in machine learning has been driven by a confluence of factors, such as the massive increase in computing power, emergence of neural networks (connected transistors that replicate the structure of neurons in the human brain) and the easy availability of vast amounts of data, thanks to the Internet. Compared to AI leaders in the Silicon Valley and China, India is a laggard but even here, nearly 300 start-ups are using some form of AI, according to Tracxn, a start-up tracker. Among dedicated AI-only Indian start-ups, 23% are working on providing solutions to multiple industries, 15% are in e-commerce, 12% in healthcare, 11% in education, 10% in financial services, and the rest in fields such as retail and logistics, according to a 2017 report by Kalaari Capital, a venture capital firm. Internet companies tap machine learning techniques for a range of uses--to recommend products for you, for instance, or to predict where cabs should be placed so that when you open your cab-hailing app, there's one a couple of minutes' drive away. Healthcare start-ups use AI to help hospitals make speedy and accurate blood reports and medical diagnoses, saving lives. Others get fashion brands and retailers to buy the right quantities of stock.

Message from the world's best StarCraft players to the world's most advanced AI: bring it on. The space-war computer game is widely regarded as the ultimate challenge for AI programs due to its complexity and rapid pace. Expectations for a match-up between a professional StarCraft player and sophisticated AI ratcheted up last year after an AI program beat a highly ranked human player at Go, one of the world's most difficult board games. At the time, a number of AI experts pointed to StarCraft as the next target for an AI-versus-man showdown. Among them: Demis Hassabis, the founder and CEO of DeepMind, the AI-focused division of Alphabet that created the triumphant Go-playing AI program, AlphaGo.

So how do we program a computer to translate human language? The simplest approach is to replace every word in a sentence with the translated word in the target language. This is easy to implement because all you need is a dictionary to look up each word's translation. But the results are bad because it ignores grammar and context. So the next thing you might do is start adding language-specific rules to improve the results.

Salesforce today announced the results of a research project tackling one of the most difficult natural language processing (NLP) research challenges--generating long, coherent, and meaningful text summaries. The new deep learning approach Salesforce researchers Romain Paulus, Caiming Xiong and Richard Socher experimented with, promises to help address the challenge of information overload we all suffer from today. "For a long time," says Socher, "text summarization has not made much progress." Summaries are typically created by either selecting specific words or phrases or by attempting to produce an abstract using words not found in the original. The results lack coherence, flow and readability.