DeepMind, Google's London-based artificial intelligence company, has started training neural networks to recognise the signs of eye disease in medical images. A partnership with Moorfields Eye Hospital in London has given the company access to about a million anonymised retinal scans, which DeepMind will feed into its artificial intelligence software. The project will target two of the most common eye diseases – age related macular degeneration and diabetic retinopathy. More than 100 million people around the world have these conditions. The information that Moorfields is providing includes scans of the back of people's eyes, as well as more detailed scans known as optical coherence tomography (OCT). The idea is that the images will let DeepMind's neural networks learn to recognise subtle signs of degenerating eye conditions that even trained clinicians have trouble spotting.

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We're only at the beginning of the development of AI technology and the potential is enormous. AI has become integral to the inner workings of most industries and it's undeniable that it's evolving at an alarming rate. It's been adopted within the mechanics of everyday life and whilst simulating human intelligence is probably not all that far off – will AI ever harness true anthropomorphic traits like the capacity to be creative? Creativity is how we express our experiences. It relies on originality, imagination and the ability to make something not done before.

Google has purchased a French startup by the name of Moodstocks, which specializes in object recognition using a smartphone. It's believed that this acquisition is part of a company effort to boost AI development. Interestingly, as noted in Engadget's report, the technology deployed by Moodstocks is able to utilize the power of the modern smartphone to recognize objects, as opposed to other solutions that require external server computing. It's said Google will absorb the engineering team at Moodstocks at its research and development center in Paris. "Today, we're thrilled to announce that we've reached an agreement to join forces with Google in order to deploy our work at scale. We expect the acquisition to be completed in the next few weeks. Our focus will be to build great image recognition tools within Google, but rest assured that current paying Moodstocks customers will be able to use it until the end of their subscription."

What if you could tap into the same level of artificial intelligence (AI) that the most advanced consumer internet companies use to run their services? The answer, apparently, is you can. That, at least, is the promise behind some of the "machine learning in the cloud" services that are now becoming widely available. Amazon Web Services (AWS), for instance, added machine learning -- the ability for computers to learn without programming -- to its list of available services last year. This gives customers the ability to apply the same algorithms to their own data that Amazon has used internally for fraud detection and to drive its website recommendations.

Deep learning has become of the most relevant trends in modern software technology. From a conceptual standpoint, deep learning is a discipline of machine learning that focuses on modeling data using connected graphs with multiple processing layers. In the last few years, deep learning has become a pivotal technology to power uses cases such as image recognition, natural language processing or even powering some of the capabilities of self-driving vehicles. The popularity of deep learning has expanded beyond just software and now the industry is starting to talk about the first generation of hardware with deep learning capabilities: a deep learning chip. A few months ago, at its I/O Conference, Google announced the design of an application-specific integrated circuit (ASIC) focused on deep learning capabilities and neural nets.

It's odd holding a minute's silence for a dead person who you never knew anything about, especially at a tech show where you can see messages from their AI reincarnation flashing up on a massive screen behind the very-much-alive man who has made this whole strange scene possible. Is this what a 21st-century Dr Frankenstein looks like? Except now, instead of stitching body parts together to recreate life, what we're seeing is found fragments of the deceased's personality put through a form of machine learning to create a semblance of their life after death. Very recently however, Luka was adapted in a brand new way, to include a chatbot based on a real human being--one who just so happens to be dead. It's this ghost-in-the-machine that has the audience spellbound, as Luka's cofounder Eugenia Kuyda explains how text messages, social media conversations, and other sources of information on the deceased were grafted onto an existing AI platform.

Your job may end up being done by a computer in the not-so-distant future, according to participants in a White House-hosted discussion on Tuesday. The discussion addressed how the future of automation can provide many social and economic advantages, but could also be a major disadvantage for those who would lose their jobs to robots. "There are fantastic things to be very excited about," said Martin Ford, author of two books on the future of automation. He cautioned that increased automation could have major detriments for the labor market. "Almost any kind of work that is on some level routine and predictable is very highly susceptible to [automation]."

The classifiers can be represented as discrimination maps, where a red color indicates that the intensity at that location contributes to the likelihood of the images belonging to the more advanced stage, and a blue color to the likelihood of belonging to the less advanced stage. Weights are shown inside the mask that resulted in the highest accuracies for each classification: A: Alzheimer's disease (AD) vs. subjective cognitive decline (SCD); B: AD vs. mild cognitive impairment (MCI); C: MCI vs. SCD. Figure 2. Discrimination maps for classifying MCI subgroups, inside the masks that resulted in the highest accuracies. A: between patients with MCI that converted to AD (MCIc) and subjects with SCD; B: between MCIc and patients with MCI that remained stable (MCIs).

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