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.

"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.

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."

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.

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.

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.

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.)

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?