A new kind of doctor has entered the exam room, but doesn't have a name. In fact, these doctors don't even have faces. Artificial intelligence has made its way into hospitals around the world. Those wary of a robot takeover have nothing to fear; the introduction of AI into health care is not necessarily about pitting human minds against machines. AI is in the exam room to expand, sharpen, and at times ease the mind of the physician so that doctors are able to do the same for their patients.

Theresa May has issued a call for international co-operation to develop ethical rules for the use of technological breakthroughs in areas like artificial intelligence (AI). Speaking at the World Economic Forum in Davos, the Prime Minister said she wanted to make the UK a world leader in innovative technologies, including AI, which could provide "a path to deliver prosperity and growth for all our people". However, she said that new norms and regulations must be developed to allay public concerns over issues like the control of private data, the disappearance of traditional jobs and the abuse of social media.

BBC Breakfast presenters Dan Walker and Louise Minchin were left in fits of giggles today as they listened to a whale being taught to mimic human speech. The hosts laughed with each other on this morning's programme as they heard how Wikie, a 16-year-old female orca has learnt to count to three. There was much amusement at the sounds of the numbers, which emerge through her blowhole as parrot-like squawks, shrill whistles or raspberries. Wikie is also said to have learnt how to say English words such as'hello' and'bye bye' and'Amy' – her trainer's name. Minchin said: 'Every time you hear it, you just can't help yourself can you?' Walker added: 'I don't know what it is about that noise, but it just does me every time.'

On a rainy September day in Frankfurt Germany, Mercedes-Benz executives and I got in a specially built semi-automated S-Class to track our drive to Stuttgart. It was the first leg in a five-month, five continent training of the automaker's self-driving system called the Intelligent World Drive that ended at CES earlier this month. It's easy to forget that road laws and regulations differ not only in other countries but even within the United States. While Californians are used to right-turns on red lights and motorcycles lane splitting on the highway, in other places, both of those are not only frowned upon, they're illegal. That means automakers not only have to learn the local laws of each region but the driving habits of the people in those areas plus varying signage and road markings.

There are already a fair number of books about Numpy (see Bibliography) and a legitimate question is to wonder if another book is really necessary. As you may have guessed by reading these lines, my personal answer is yes, mostly because I think there is room for a different approach concentrating on the migration from Python to Numpy through vectorization. There are a lot of techniques that you don't find in books and such techniques are mostly learned through experience. The goal of this book is to explain some of these techniques and to provide an opportunity for making this experience in the process. Disclaimer: All external pictures should have associated credits.

In late 2016 Prof Geoffrey Hinton, the godfather of neural networks, said that it's "quite obvious that we should stop training radiologists" as image perception algorithms are very soon going to be demonstrably better than humans. Radiologists are, he said, "the coyote already over the edge of the cliff who hasn't yet looked down". This kick-started a hype-wave of biblical proportions that is still rolling to this day, and shows no signs of breaking just yet. In my opinion, although this wave of enthusiasm and optimism has successfully brought radiology artificial intelligence to the forefront of people's imaginations, and immense amounts of funding with it, it has also done untold harm by over-inflating the expectations of policy and decision makers, and is having tangible knock-on effects on recruitment as disillusioned junior doctors start believing that machines are indeed replacing humans and so they shouldn't bother applying to become radiologists. It is hard to imagine a more damaging statement occurring at a time when the crisis in radiology staffing, especially acute in the UK, is threatening to destabilise entire hospital systems.

The government is searching for someone to become the "highly public face for AI" as head of its new Office for Artificial Intelligence (OAI). The OAI, the formation of which was announced in the Industrial Strategy whitepaper published in November, will be jointly established and run by the Department for Digital, Culture, Media and Sport and the Department for Business, Energy and Industrial Strategy. The two Whitehall departments have launched a recruitment process for a leader to head up the office. The role – which comes with a salary of up to £80,000 – is being offered on an initial two-year fixed-term, loan, or secondment contract, "with the possibility of extension or permanency", the government said. The head of OAI will be employed by DCMS but will report into the senior managers and ministers of both departments.

French public relations firm Publicis Groupe and Microsoft have partnered to develop Marcel, a new system that uses cognitive services and artificial intelligence to improve the way the group's teams and clients connect and work together. Publicis.Sapient, the group's technology arm, is defining the architecture, design and user experience. Microsoft will build the platform, leveraging Microsoft Azure AI and Office 365. The Marcel platform will identify relevant connections amongst Publicis Group's 80,000 employees and enable employees to take advantage of the combined experiences that exist around its group. Publicis Groupe has been testing new collaborative models that are already driving results for some of its major global clients.

DAVOS, SWITZERLAND – When global leaders and captains of industry gathered in Davos last week for the 2018 World Economic Forum, AI and the economy of tomorrow dominated the discussions. Coupled with the launch of two new reports into the future of work at the summit, it is clear that AI and reskilling are already central considerations for policymakers and businesses alike. This much was evident in the coverage of the event. Both the British Prime Minister Theresa May and French President Emmanuel Macron announced government-funded innovation measures around securing'safe and ethical AI'. Meanwhile, Google CEO Sundar Pichai boldly claimed that artificial intelligence will prove more important to the fate of humanity than our mastery of fire, and Alibaba founder Jack Ma argued automation technologies could'kill a lot of jobs'.

AI growth has dominated most of last year's technology news, from algorithms learning how to play Go without human input, through Elon Musk and Mark Zuckerberg disputing the dangers and benefits of AI to humanity, down to Russia and China declaring it to be a top priority. Looking ahead, 2018 will be a year of developing and deepening all available AI technologies. Deep learning, one of the most important subfields of research on artificial intelligence, will be especially promising. Generally speaking, the goal of artificial intelligence is to make computers as smart, or even smarter than human beings, by giving them human-like thinking and reasoning abilities. Among the many ways to achieve this, machine learning AI is the baseline technique, used widely across all industries.