The question may seem basic, but the answer is kind of complicated. In the broadest sense, AI refers to machines that can learn, reason, and act for themselves. They can make their own decisions when faced with new situations, in the same way that humans and animals can. As it currently stands, the vast majority of the AI advancements and applications you hear about refer to a category of algorithms known as machine learning (see "What is machine learning?"). These algorithms use statistics to find patterns in massive amounts of data.

While usually you would use world-based algorithms, the team suggests that that method is at fault due to the fact that they treat all possible worlds equally, despite the negative effects some may cause, and that they do not well-utilize the consistency among possible worlds that is there. The team introduces a representative possible world-based consistent clustering algorithm for this type of uncertain data, with results showing better than other state-of-the-art algorithms.

At Leia we are working at the forefront of display technology -- making screens come to life in richer, deeper, and more beautiful ways. We apply our proprietary nanotechnology to give any display the ability to produce lightfield, immersive "holographic" content -- no glasses, no tracking, no fuss. Through our digital platform LeiaLoft, we also aim to create an environment where developers and artists of the future will bring their creativity to life and deliver breathtaking experiences to the world at large. Come join a team of passionate scientists, engineers and artists in a beautiful adventure where you will be defining the digital experiences of tomorrow. As a Software Engineer, Machine Learning with the Computer Vision group, you will develop software and models that marries cutting-edge computer vision and deep learning with Leia's lightfield technology.

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An innovative chat-bot that helps patients and doctors diagnose diseases ranging from malaria to diabetes has become the first health app to launch in Swahili. Developed by Ada Health, the app relies on artificial intelligence, large medical databases and personalised responses to assess an individual's symptoms, suggest a cause and recommend the next stage of treatment. The smartphone chat-bot is already used by roughly eight million people in more than 130 countries across the globe – published in languages including English, French and Spanish. But it has now become the first AI health application to launch in Swahili, a language spoken by almost 100 million people across East Africa – predominantly in Tanzania, Uganda and Kenya. According to Hila Azadzoy, the managing director of Ada's global health initiative, the expansion will help tackle a shortage of doctors and nurses in the region, where countries have fewer than one physician per 1,000 people on average.

Now scientist from Italian research institute SISSA and the Technical University of Munich have found a light to shine inside – an approach for studying deep neural networks that reveals the processes that they are able to carry out – so long as they are image processing networks. "We have developed a method to systematically measure the level of complexity of the information encoded in the various layers of a deep network – the so-called intrinsic dimension of image representations," according to SISSA scientists Davide Zoccolan and Alessandro Laio. "Thanks to the collaboration of experts in physics, neurosciences and machine learning, we have exploited a tool originally developed in another area to study the functioning of deep neural networks". Working with Jakob Macke, of TUMunich, they applied the method to find out that, inside an image recognition deep neural network, representations of the image undergo a progressive transformation. Similar to what happens in the visual system, they analyse content progressively, through a chain of processing stages.

The healthcare industry is at a crucial juncture in the field of medical robotics. We are standing at the edge of a significant shift in the way we interact with the world and go about living our daily lives. Every day, innovations are being made which are inevitably pushing us towards a future where the majority of work will be automated or instead performed by robots. The rise of automation and replacement of the working class with robots or machinery is not something that is necessarily'New'. It is an issue as old as the concept of technology that has begun to rear its head in the last decade or so.

The book Rebooting AI explains why a different approach other than deep learning is needed to unlock the potential of AI. Authors Gary Marcus and Ernest Davis propose that AI programs will have to have a large body of knowledge about the world in general, represented symbolically. Some of the basic elements of that knowledge should be built in. InfoQ readers can read excerpts of Rebooting AI to get an impression of the book. InfoQ interviewed Marcus and Davis about the state of the practice of AI and main concerns, the limitations of deep learning and their suggestion for bringing "common sense" to machine learning, what's needed to make AI safe and trustworthy, and what they expect AI can bring us in the near future and what will take a longer time.

Uber Eats has been one of the fastest-growing food delivery services since the initial launch in Toronto in December 2015. Currently, it's available in over 600 cities worldwide, serving more than 220,000 restaurant partners and has reached 8 billion gross bookings in 2018. The ability to accurately predict delivery times is paramount to customer satisfaction and retention. Additionally, time predictions are important on the supply side as we calculate the time to dispatch delivery partners. My recent talk covered how Uber Eats has leveraged machine learning to address these challenges. With the mission "Make eating well effortless, every day, for everyone" one of our top priorities is ensuring reliability.

With global credit card fraud loss on the rise, it is important for banks, as well as e-commerce companies, to be able to detect fraudulent transactions (before they are completed). According to the Nilson Report, a publication covering the card and mobile payment industry, global card fraud losses amounted to $22.8 billion in 2016, an increase of 4.4% over 2015. This confirms the importance of the early detection of fraud in credit card transactions. Fraud detection in credit card transactions is a very wide and complex field. Over the years, a number of techniques have been proposed, mostly stemming from the anomaly detection branch of data science. In the first scenario, we can deal with the problem of fraud detection by using classic machine learning or statistics-based techniques. We can train a machine learning model or calculate some probabilities for the two classes (legitimate transactions and fraudulent transactions) and apply the model to new transactions so as to estimate their legitimacy. All supervised machine learning algorithms for classification problems work here, e.g., random forest, logistic regression, etc.