Stock market cheats, A.I. will soon be looking for you. The sheer volume of transactions conducted in financial markets renders market surveillance by regulatory groups difficult, but machine learning and artificial intelligence tools will soon be employed to ferret out cheaters, according to Reuters. The Financial Industry Regulatory Authority (FINRA) is developing A.I. that it will start testing in 2017 along with its existing surveillance and detection mechanisms. NASDAQ and the London Stock Exchange Group intend to start using artificial intelligence to spot trade irregularities and violation patterns this year. Related: Future AI assistants like Siri could be trained by Joey from'Friends' Reuters reports that financial firms already use artificial intelligence for picking stocks and for monitoring their own firms' compliance.

Encryption is something we all rely on regularly to keep our information safe online, but many of us have experienced it since childhood, and in fact probably used it in school. If you ever wrote out a message in code that nobody could read without they knew the decipher rules, you messed around with encryption! That same secret message technique has now been put to a much more worrying use. Google has created multiple AI and they've learned how to not only create their own encryption, but are now communicating using messages nobody else can read. This Google Brain project is an experiment in deep learning techniques and involved the use of three neural networks (Alice, Bob, and Eve) created using artificial neurons.

As the New Scientist reports, Abadi and Andersen assigned each AI a task: Alice had to send a secret message that only Bob could read, while Eve would try to figure out how to eavesdrop and decode the message herself. The experiment started with a plain-text message that Alice converted into unreadable gibberish, which Bob could decode using cipher key. At first, Alice and Bob were apparently bad at hiding their secrets, but over the course of 15,000 attempts Alice worked out her own encryption strategy and Bob simultaneously figured out how to decrypt it. The message was only 16 bits long, with each bit being a 1 or a 0, so the fact that Eve was only able to guess half of the bits in the message means she was basically just flipping a coin or guessing at random. Of course, the personification of these three neural networks oversimplifies things a little bit: Because of the way the machine learning works, even the researchers don't know what kind of encryption method Alice devised, so it won't be very useful in any practical applications.

If pop culture is to be believed, society is quickly heading toward a highly automated future ruled by artificial intelligence. Take Iron Man's trusty sidekick, J.A.R.V.I.S. Within the Marvel franchise, the artificial intelligence system is able to think, act, and feel like a human. The supporting character is even sarcastic and witty -- both trademark human characteristics. In some ways, J.A.R.V.I.S. seems like a better human than most humans. With the release of AI technologies like IBM Watson and Salesforce Einstein, in addition to the recent buzz about the "Partnership on AI," which has brought together some of the world's biggest tech companies to advance research in the sector, it might seem like that fantasy is quickly turning into reality.

In less than 12 hours, three different people offered to pay me if I'd spend an hour talking to a stranger on the phone. All three said they'd enjoyed reading an article I'd written about Google building a new computer chip for artificial intelligence, and all three urged me to discuss the story with one of their clients. Each described this client as the manager of a major hedge fund, but wouldn't say who it was. The requests came from what are called expert networks--research firms that connect investors with people who can help them understand particular markets and provide a competitive edge (sometimes, it seems, through insider information). These expert networks wanted me to explain how Google's AI processor would affect the chip market.

The study was a success: the first two AIs learnt how to communicate securely from scratch. P input plaintext, K shared key, C encrypted text, and PEve and PBob are the computed plaintext outputs.The Google Brain team (which is based out in Mountain View and is separate from in London) started with three fairly vanilla neural networks called Alice, Bob, and Eve. Each neural network was given a very specific goal: Alice had to send a secure message to Bob; Bob had to try and decrypt the message; and Eve had to try and eavesdrop on the message and try to decrypt it. Alice and Bob have one advantage over Eve: they start with a shared secret key (i.e. this is symmetric encryption). Importantly, the AIs were not told how to encrypt stuff, or what crypto techniques to use: they were just given a loss function (a failure condition), and then they got on with it.

Can computer software be designed to be more emotional? Imagine the idea of conversing with your computer, perhaps checking the weather. The weather appears to be cold and rainy for the early part of the day. This naturally brings a certain feeling of negativity, perhaps even dread, to most people. Typical computer programs of today will simply report the weather and prompt for the next query, without giving the user's disposition a single thought.

It's not something to be sniffed at. Computers have cracked a problem that has stumped chemists for centuries: predicting a molecule's odour from its structure. The feat may allow perfumers and flavour specialists to create new products with much less trial and error. Unlike vision and hearing, the result of which can be predicted by analysing wavelengths of light or sound, our sense of smell has long remained inscrutable. Olfactory chemists have never been able to predict how a given molecule will smell, except in a few special cases, because so many aspects of a molecule's structure could be important in determining its odour.

In yesterday's article we talked about the 3rd gene editing company getting ready to IPO, CRISPR Therapeutics. While gene editing is an incredibly complex technology, the whole idea behind it is actually quite simple. You can use gene editing technology (read about the three main types here) to edit a gene and then start changing the way life forms actually work. When you start to play around with modifying life forms in this way, then this is an area of research we refer to as "synthetic biology". The whole gene editing/synthetic biology space is incredibly exciting because the possibilities are really infinite.

Not surprisingly, interest in this opportunity went through the roof. With only 500 seats initially available, and applications already covering that number multiple times, the competition to reserve a spot in the workforce of the future, especially given the fact that graduates will be given preference by Nanodegree including Didi Chuxing and IBM Watson, is guaranteed to be fierce. For graduates of the 2011 AI class, Introduction to Artificial Intelligence, the fact that the instructor line up reunites Sebastian Thrun and Peter Norvig is another incentive to enrol.