In AI, is data more important than algorithms? In AI, is data more important than algorithms? I hope you are not expecting a simple black or white answer to this question. Whether data or algorithms are more important has been debated at length by experts (and non-experts) in the last few years and the short version is that it depends on many details and nuances that take some time to understand. I answered a pretty similar question some time ago in this Quora post: In machine learning, is more data always better than better algorithms?

IBM said it is offering support for Google's open source machine learning technology TensorFlow with its PowerAI software. The next wave of IT innovation will be powered by artificial intelligence and machine learning. We look at the ways companies can take advantage of it and how to get started. The move highlights ongoing collaboration between tech giants in the AI space including Google, Nvidia, IBM, and AMD. According to IBM, TensorFlow has become popular among enterprises looking to use deep learning frameworks.

Medical diagnosis has been identified as one of the areas where artificial intelligence and machine learning could have most impact – but how should the NHS proceed? The likes of University College London Hospitals NHS Foundation Trust, Royal Free NHS Trust and Moorfields Eye Hospital have teamed up with Google DeepMind to work on better patient outcomes for those suffering from certain cancers, or for those who have had sight loss. According to Orlando Agrippa, deputy CIO at Barts Health NHS Trust, the NHS could benefit tenfold if it leveraged AI. "It could be used to increase accuracy over things like prescriptions, interventions and early diagnosis," he told BusinessCloud. The Commons Science and Technology Committee suggested last year that supercomputers assisting doctors with medical diagnoses could be one of the key impact areas of AI, but that government leadership in the fields of robotics and AI had been lacking.

London-based startup Digital Genius is targeting the contact centre with its deep learning technology. The Digital Genius neural network helps agents respond to common customer service queries quicker by first automatically sorting and labelling the metadata and then generating three potential responses, each with a level of certainty attached. Organisations can then set a threshold for automation, so responses the system views as 90 percent accurate could be automatically sent out - like a chatbot - and anything below is sent to the agent to review, for example. The startup claims it can reduce the average handling time for cases by 20 percent. This is especially important as organisations receive queries from more and more digital channels, including social media.

Artificial intelligence is getting more than its share of proponents and detractors. But the underlying question for many lay persons is what exactly is AI? Is it humanoid robots assisting elderly populations in their day-to-day routines, or a machine that can filter through infinite research data to create the next life-saving drug? Is it the human-like ability of a system to filter and block spam in the blink of an eye, or a vacuum that knows when and where to do its work without human intervention? The answer is of course all of the above – and much, much more.

If you're worried about the possibility of skin cancer, you might not have to depend solely on the keen eye of a dermatologist to spot signs of trouble. Stanford researchers (including tech luminary Sebastian Thrun) have discovered that a deep learning algorithm is about as effective as humans at identifying skin cancer. By training an existing Google image recognition algorithm using over 130,000 photos of skin lesions representing 2,000 diseases, the team made an AI system that could detect both different cancers and benign lesions with uncanny accuracy. In early tests, its performance was "at least" 91 percent that of its flesh-and-blood counterparts.

Three years ago, artificial intelligence research firm DeepMind was acquired by Google for a reported £400m. As part of the acquisition, Google agreed to set up an ethics and safety board to ensure that its AI technology is not abused. The existence of the ethics board wasn't confirmed at the time of the acquisition announcement, and the public only became aware of it through a leak to industry news site The Information. But in the years since, senior members of DeepMind have publicly confirmed the board's existence, arguing that it is one of the ways that the company is trying to "lead the way" on ethical issues in AI. But in all that time DeepMind has consistently refused to say who is on the board, what it discusses, or publicly confirm whether or not it has even officially met. The Guardian has asked DeepMind and Google multiple times since the acquisition on 26 January 2014 for transparency around the board, and received just one answer on the record.

DeepTraffic is a gamified simulation of typical highway traffic. Your task is to build a neural agent – more specifically design and train a neural network that performs well on high traffic roads. Your neural network gets to control one of the cars (displayed in red) and has to learn how to navigate efficiently to go as fast as possible. The car already comes with a safety system, so you don't have to worry about the basic task of driving – the net only has to tell the car if it should accelerate/slow down or change lanes, and it will do so if that is possible without crashing into other cars. The page consists of three different areas: on the left you can find a real time simulation of the road, with different display options, using the current state of the net.

Researchers at Stanford University have created an AI algorithm that can identify skin cancer as well as a professional doctor. The program was trained on nearly 130,000 images of moles, rashes, and lesions using a technique known as deep learning. It was then tested head-to-head against 21 human dermatologists, where its creators say it performed with an accuracy on par with humans ("at least" 91 percent as good). In the future, they suggest it could be used to create a mobile app for spotting skin cancer at home. Each year in the United States, some 5.4 million new cases of skin cancer are diagnosed.

One of the big topics that was discussed in financial technology (fintech) circles at length in 2016 ― and will continue to be discussed at even greater length as we enter 2017 ― is artificial intelligence (AI). Machine learning is emerging as the field within AI that is seeing the most amount of real-world applications and use cases among financial institutions, especially in the area of fraud. Take, for example, a historic event that unfolded in March 2016 that demonstrated the power of machine learning: the victory of program AlphaGo of Google DeepMind over professional gamer Lee Se-dol. This exciting technological breakthrough demonstrates how far AI has come, even in just the past year, and how it is now able to catch humans out. It's true, though, that the idea of computers learning autonomously has been around for decades.