What shortcomings do you see with deep learning? Deep Learning has been incredibly successful in recent years, but it is still merely a tool for classifying items into categories (or for nonlinear regression). We have seen outstanding results in mapping images, audio segments, even board positions, into categories with ever-increasing accuracy, but AI needs to go way beyond classification and regression. Let's talk about AlphaGo, which is a phenomenal technical achievement by the team at DeepMind. Yet, the overblown claims about the impressive success of AlphaGo are a case of a person climbing to the top of the tree and shouting "I'm on my way to the moon!"

Every 60 seconds, Facebook users share 2,460,000 pieces of content and Yelp receives 26,380 reviews. Then there's the 2 million blog posts created each day and the 1 billion websites available for us to peruse. With all of this content floating around the internet, marketers struggle to truly engage and convert an increasingly fragmented online audience. Using Watson services, Opentopic helps clients solve that problem in a 2-step process. First, they help the client discover and analyze target audiences.

When Satya Nadella made machine learning the centerpiece of the Microsoft Build conference, I think it became official: 2016 is the year of machine learning. All the major clouds now have (or will soon have) machine learning APIs. In fact, InfoWorld's Martin Heller has already reviewed the machine learning services offered by AWS, Azure, and IBM Cloud. Even more telling, just a couple of years ago only a handful of machine learning startups were out of stealth; now there are, what, a thousand? Oddly, Nadella's Build talk revolved around intelligent bots, which operate a little like today's highly annoying interactive voice response systems.

In future, machine learning could improve transport, security, healthcare and revolutionise industry. You'll also be able to take part in an interactive exhibition where machine learning developers and researchers will showcase examples of the technology in action. The talks and panel discussion will run from 18:30 to 20:00, doors open at 17:30 and close at 21:00. Tweet your questions to @royalsociety using #rsmachinelearning during the panel discussion to join the conversation.

I saw a news article recently about how high frequency trading firms are turning to artificial intelligence (AI) to improve their trading performance and profit. Two large companies were mentioned with each having a slightly different approach. This May, Nomura Securities are releasing a new stock trading system, built around analyzing and assessing vast price and trading data resources. The system aims to simulate the insights of experienced (human) traders and make predictions based on historical market conditions and the correlation to current asset prices. Nothing particularly new about that, but the difference is they will be using AI tools to enable the system to enhance its price prediction ability as it gains more experience.

The secret ingredient that sets AI4Cloud apart from all our competitors is in how we leverage the patented distance-vector based Analytics and other Intelligence capabilities inside our software products. This unique capability allows AI4Cloud to bring to our customers software solutions with embedded human intelligence, creating an actual "thinking" machine dedicated to solving customer problems. Our Solutions take the knowledge of brilliant professionals about how IT objects behave and embed this intelligence directly into our software deployments. That's why we call it "Smarts"! Our unique solution provides a Manager of Managers and monitors the Data & Voice Network, Server, Storage database & application infrastructure for fault, availability, performance, and capacity management to provide proactive, assured high levels of service, and reduces impacts to business services for our customers.

It's almost inevitable, given dire warnings about the threat to the human race by some prominent commentators, including a billionaire and visionary entrepreneur, and one of the world's most prominent physicists, that you would associate Artificial Intelligence (AI) with the rise of the machines, aka, Skynet, aka the Terminator. Futurists predict that it's only a matter of time before we create a computer that's smarter than the human brain, and after that the very smart machines can create even smarter machines, eventually leapfrogging the capacity of human intelligence. It doesn't take a Hollywood imagination to predict the doomsday scenario of super smart computers creating machines with capabilities vastly beyond the human ken – after all, even the lowliest computer today can out-compute, at vastly superior speeds, the average human. Humans however, have always been able to think, and therefore outsmart fast, efficient computers programmed to do whatever they're programmed to do. Cognitive computing, artificial intelligence, machine learning systems, call it what you will, is on the cusp of a technology explosion.

I have been studying Artificial Intelligence (AI) for Capital Markets for ten months now and I am shocked everyday by the speed of evolution of this technology. When I started researching this last year I was looking for the Holy Grail trading tools and could not find them, hence I settled for other parts of the trade lifecycle where AI solutions already existed. Yesterday, as I was preparing for a speech on AI at a conference, one of my colleagues in Tokyo forwarded me an Asian newswire mentioning that Nomura securities, after two years of research, would be launching an AI enabled HFT equity tool for its brokerage institutional clients in May – here it is: the Holy Grail exists, and not only at Nomura. Other brokers have been shyly speaking about their customizable smart brokerage, e.g. Some IBs are working on that, they just don't publicly talk about it.

Security experts have devised a method that allows users to recover data from computers infected with the Petya ransomware program without paying money to cybercriminals. Petya appeared on researchers' radar last month when criminals distributed it to companies through spam emails that masqueraded as job applications. It stood out from other file-encrypting ransomware programs because it overwrites a hard disk drive's master boot record (MBR), leaving infected computers unable to boot into the operating system. The program replaces the drive's legitimate MBR code, which normally starts the operating system, with code that encrypts the master file table (MFT) and shows a ransom note. The MFT is a special file on NTFS volumes that contains information about all other files: their name, size and mapping to hard disk sectors. The actual contents of the user's files are not encrypted, but without the MFT, the OS no longer knows where those files are located on disk.

Accenture's Fintech Innovation Lab initiative is an accelerator programme designed to put the best fintech start ups in front of potential banking customers and investors - we interview those that made it to the final. Quotip: "We help to introduce investors into the financial product industry and help them find ideas in any product environment by using a machine learning based algorithm. "Traditional banks have a big trading desk for pricing products but for many it is not economical to do that in the same way. So we came up with an idea for machine learning to extract information from the exchange. "In Switzerland there are lots of products listed on an exchange, if you look at them there is something like 35,000 on the Swiss exchange. We will extract information from the prices that are fed from investment banks to the exchange and with this information we can incorporate pricing and feed that back to customers."