Many of the biggest tech companies are joining forces to ease fears around artificial intelligence. Facebook (FB, Tech30), Amazon (AMZN, Tech30), Google (GOOGL, Tech30), Microsoft (MSFT, Tech30) and IBM (IBM, Tech30) have formed a new nonprofit to establish best practices for the development of AI technology in partnership with academics and ethics experts. The group, unveiled on Wednesday, goes by the heartwarming name Partnership on Artificial Intelligence to Benefit People and Society. "This partnership will ensure we're including the best and the brightest in this space in the conversation to improve customer trust and benefit society," Ralf Herbrich, director of machine learning science and core machine learning at Amazon, said in a statement. Each of the corporate members is expected to make financial and research contributions to the group.

Summary: At the core of modern AI, particularly robotics, and sequential tasks is Reinforcement Learning. Although RL has been around for many years it has become the third leg of the Machine Learning stool and increasingly important for Data Scientist to know when and how to implement. If you poled a group of data scientist just a few years back about how many machine learning problem types there are you would almost certainly have gotten a binary response: problem types were clearly divided into supervised and unsupervised. While Reinforcement Learning (RL) has been around since at least the 80's and before that in the behavioral sciences, its introduction as a major player in machine learning reflects it rising importance in AI. What problems fit this description?

"A rose by any other name would smell as sweet," to paraphrase a famous bard. There is a huge ongoing boom in messaging-based applications coming to market, generically called "bots." Regular readers of technology sections are no doubt also aware that, by most accounts, the performance and adoption of these bots has failed to match the hype. Is the technology premature, are businesses rushing in too quickly, or are bots inherently limited? The first step to answering that question is deciding what "bot" even means.

The world's biggest technology companies are joining forces to consider the future of artificial intelligence. Amazon, Google's DeepMind, Facebook, IBM and Microsoft will work together on issues such as privacy, safety and the collaboration between people and AI. Dubbed the Partnership on Artificial Intelligence, it will include external experts. One said he hoped the group would address "legitimate concerns". "We've seen a very fast development in AI over a very short period of time," said Prof Yoshua Bengio, from the University of Montreal.

Machine Learning, driven by the rise of big data and evolving technologies, is emerging as a powerful quantitative investment tool, with financial advisers increasingly recognising the benefits it can bring to investors, according to Man AHL. A recent survey of financial advisers, who attended a series of adviser events held by Man AHL across Australia earlier this month, found that nearly three quarters of advisers (74%) believe Machine Learning, a branch of artificial intelligence, has the potential to change the way we invest in the future. Speaking at the adviser events, Man AHL's Oxford-based Chief Scientist Dr. Anthony Ledford said that Machine Learning has become increasingly important to the alternative investment management industry as it deals with larger and more complex data-sets. "The rise of Machine Learning in quantitative investing is powered by three separate revolutions: the growth in computing power, the explosion of data generation and the maturing of methodologies from statistics, computer science, mathematics and engineering, amongst other disciplines." "As more data become available, sophisticated Machine Learning models enable new patterns to be detected that humans can't easily spot. The technology is a significant area of research focus for Man AHL and we believe our enhanced focus on Machine Learning will be strongly supportive of the evolution of our quantitative investment strategies," Dr. Ledford said.

However, in practice, RNNs can be hard to train and for small to medium-sized training datasets, "good old" methods can often deliver similar or even superior performance at a lower computational cost. Even in the Deep Learning category, RNNs have a strong competitor in Convolutional Neural Nets (a.k.a. ConvNets or CNNs) - just as long as your text can be treated as fixed length sequences, making them a suitable approach to represent and classify tweets, text messages, short user reviews, etc. Still, it's too early to dismiss RNNs and their variants entirely. Where these networks (and particularly their more advanced variant called Long-Short Memory Networks or LSTMs) begin to shine are other NLU tasks that often involve prediction (i.e., generative in nature) rather than "just" classification, a fundamentally discriminative task.

When the writer Rebecca Forster first heard how Google was using her work, it felt like she was trapped in a science fiction novel. "Is this any different than someone using one of my books to start a fire? I have no idea," she says. "I have no idea what their objective is. Certainly it is not to bring me readers."

One of the most difficult emerging problems when it comes to artificial intelligence is making sure that computers don't act like racist, sexist dicks. As it turns out, it's pretty tough to do: humans created and programmed them, and humans are often racist, sexist dicks. If we can program racism into computers, can we also train them to have a sense of fairness? Some experts believe that the large databases used to train modern machine learning programs reproduce existing human prejudices. To put it bluntly, as Microsoft researcher Kate Crawford did for the New York Times, AI has a white guy problem.

Google has developed a new version of its Translate tool and according to the company, it's almost as good as human translation. The app, like many other computer-powered translation services, lets tourists or people abroad for business speak in their own language and then translates it into that of the country they're in. However, comically bad mistranslations are common and are seen as inevitably associated with automated translation. The new software Google is rolling out, which starts with a Mandarin to English version today, should change that. Google calls the new method Neural Machine Translation, says Quartz, and it is "radical" change from the previous system.

Arik Hesseldahl is a veteran journalist with more than 20 years experience covering world-changing technology companies and trends for high profile media properties. Living in San Francisco for a few years, you learn a few things about the fall: First, the weather tends to be hotter and sunnier than the summer months. Second, you learn to avoid the area around the area around the Moscone Convention Center in late September and early October. That's when the software giants Oracle and Salesforce hold their almost back-to-back annual conferences that draw thousands of software developers. The two compete to see who can throw the more epic parties complete with big name musical acts like Aerosmith (Oracle last year) and U2 (Salesforce this year.)