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.)

A company called Waverly Labs is working on a wearable translation device that has captured people's imaginations, generated plenty of buzz, andraised over 3 million on Indiegogo. That two people wearing earpieces made by the company could speak in different languages, and with the earpieces working in conjunction with a smartphone app, each person could hear a translation in their preferred language in their ear. For example, one person could speak Spanish, and the other would hear it as English in his ear, and vice versa. The idea is undoubtedly exciting to anyone who has felt the limits of a language barrier. And a video that the company posted that demonstrates the device in action, translating between English and French, has been viewed over 280,000 times.

Over the past four years, readers have doubtlessly noticed quantum leaps in the quality of a wide range of everyday technologies. Most obviously, the speech-recognition functions on our smartphones work much better than they used to. When we use a voice command to call our spouses, we reach them now. We aren't connected to Amtrak or an angry ex. In fact, we are increasingly interacting with our computers by just talking to them, whether it's Amazon's Alexa, Apple's Siri, Microsoft's Cortana, or the many voice-responsive features of Google.

Many speeches at AI conferences begin with AlphaGo, the Google-built AI that beat Lee Sedol - one of the highest ranked Go players in the world – to illustrate how far AI has progressed. Most speakers briefly talk about how the computer programme works, and then go on to praise the intelligence of the machine. Oren Etzioni, CEO of the Allen Institute for Artificial Intelligence (AI2), however, gave the credit to the developers and not to AlphaGo. The famous science fiction writer, Arthur C Clarke, once said: "Any sufficiently advanced technology is indistinguishable from magic." "But deep learning is not magic," Etzioni said. "99 per cent of machine learning - or deep learning - is human work. AlphaGo is actually very limited, Etzioni told The Register. "If I asked AlphaGo, can you play poker?

Building Game Bot with OpenAI's Gym (Live Coding starts at 15:00) (youtube.com) I'm really loving what this guy has been doing for making deep learning and machine learning more accessible and fun.

We consider the problem of multiple agents sensing and acting in environments with the goal of maximising their shared utility. In these environments, agents must learn communication protocols in order to share information that is needed to solve the tasks. By embracing deep neural networks, we are able to demonstrate end-to-end learning of protocols in complex environments inspired by communication riddles and multi-agent computer vision problems with partial observability. We propose two approaches for learning in these domains: Reinforced Inter-Agent Learning (RIAL) and Differentiable Inter-Agent Learning (DIAL). The former uses deep Q-learning, while the latter exploits the fact that, during learning, agents can backpropagate error derivatives through (noisy) communication channels.

Apple has refused to join the likes of Google and Facebook on a new consortium that will aim to ensure artificial intelligence (AI) technology is developed in a safe, ethical, and transparent manner. The consortium -- announced on Wednesday and called Partnership on AI -- also counts Amazon, Microsoft, and IBM among its founding members. But Apple's playing hard to get. Eric Horvitz, technical fellow and managing director at Microsoft Research, said: "We've been in discussions with Apple and they're enthusiastic about the effort. I personally hope to see them join."

Too often, machine learning requires a massive investment of time and terabytes of data before it can deliver meaningful insights. But that doesn't have to be the case. A well-configured machine learning analytics tool can rapidly provide initial results -- a key advantage for developers who can then start using those results to create value. To understand this dynamic, companies have to start taking a data approach that embraces the fact that what is driving companies in today's connected world is not data, but insight. And the volume of data being created in today's connected world is not just what powers this insight…but also blocks you from finding it.