Artificial intelligence is everywhere: your smartphone, on streaming platforms such as Spotify and Netflix and even in some smart home appliances. But can the technology, which has seemingly caught the attention of most VCs across the world, be used in the realm of healthcare to drive efficiency and optimise patient outcomes? We take a look at some of the UK's most promising companies using AI to transform the healthcare space. No list of this kind would be complete without a mention of DeepMind, a British artificial intelligence company founded in 2010 and acquired by tech giant Google for a reported £400m four years later. DeepMind Health is leveraging machine learning technology – a form of AI – to boost the medical research field.

"Exponential data problems is very challenging and it's why it's hard to apply machine learning to genomics" Last week at the Deep Learning Summit in San Francisco we had lots of great speakers including Brendan Frey, Co-Founder & CEO at Deep Genomics; Andrew Tulloch, Research Engineer at Facebook and Andrej Karpathy, Research Scientist at OpenAI, amongst many others. Incase you missed the presentation from Brendan Frey from Deep Genomics, we are sharing with you the full recording of the video below! Deep Genomics bring together machine learning and experimental biology. Their systems "predict the molecular effect of genetic variation, opening a new and exciting path to discovery for disease diagnostics and therapies." Brenden talks about the recently developed gene editing systems that has made it possible to edit our genomes.

A framework is presented for unsupervised learning of representations based on infomax principle for large-scale neural populations. We use an asymptotic approximation to the Shannon's mutual information for a large neural population to demonstrate that a good initial approximation to the global information-theoretic optimum can be obtained by a hierarchical infomax method. Starting from the initial solution, an efficient algorithm based on gradient descent of the final objective function is proposed to learn representations from the input datasets, and the method works for complete, overcomplete, and undercomplete bases. As confirmed by numerical experiments, our method is robust and highly efficient for extracting salient features from input datasets. Compared with the main existing methods, our algorithm has a distinct advantage in both the training speed and the robustness of unsupervised representation learning. Furthermore, the proposed method is easily extended to the supervised or unsupervised model for training deep structure networks.

As companies increase their efforts in retaining customers, being able to predict accurately ahead of time, whether a customer will churn in the foreseeable future is an extremely powerful tool for any marketing team. The paper describes in depth the application of Deep Learning in the problem of churn prediction. Using abstract feature vectors, that can generated on any subscription based company's user event logs, the paper proves that through the use of the intrinsic property of Deep Neural Networks (learning secondary features in an unsupervised manner), the complete pipeline can be applied to any subscription based company with extremely good churn predictive performance. Furthermore the research documented in the paper was performed for Framed Data (a company that sells churn prediction as a service for other companies) in conjunction with the Data Science Institute at Lancaster University, UK. This paper is the intellectual property of Framed Data.

Investors kind of have crush on NVIDIA Corporation (NASDAQ:NVDA). The company has posted quarter after quarter of strong sales, grown gaming GPU market share, introduced new driverless car technologies, and expanded its artificial intelligence (AI) opportunities -- all of which have led investors to swoon to the stock, pushing it up over 200% over the past 12 months. If you're bullish on NVIDIA's prospects in gaming, AI, and driverless cars, then perhaps you should give Amazon (NASDAQ:AMZN), Tesla (NASDAQ:TSLA), and Sony (NYSE:SNE) a good look as well. These companies aren't making the exact same moves as NVIDIA, but each is poised to dominate one of these segments in their own way. NVIDIA is already taking big steps to make AI a priority through its investments in deep learning technologies like Drive PX 2 (for cars) and servers (DGX-1).

While artificial intelligence, machine learning and other futuristic-seeming technologies have been resigned to the likes of Apple, Google, Microsoft, Amazon and Facebook, traditional companies are also getting in the game, including Equifax and SAS. According to a report, Equifax is using deep-learning tools to enhance its credit scoring system, and SAS is using deep learning to improve its data mining tools and provide deep learning APIs. In an interview, Peter Maynard, senior vice president of global analytics at Equifax, said the company realized a few years ago that it wasn't getting enough "statistical lift" from its traditional credit scoring methods and thus started to embrace advanced deep-learning technology. The report noted that modern machine-learning technologies, such as deep neural networks, which boast much more accurate results, were perceived to not be interpretable, posing a challenge for any company wanting to use them. The complexity also added another layer of challenge for Equifax.

Last November, when Google announced that machine learning research luminary Fei-Fei Li, Ph.D. would join Google's Cloud Group Platform group, a lot was known about her academic work. But Google revealed little about why she was joining the company except she would lead machine learning for the Google Cloud business. After five months of suspense, yesterday Li revealed the focus of her new role during her keynote address at Google's cloud developer conference, Cloud Next 2017. She will apply her experience to democratize machine learning to the enterprise. Her task: Study the problems that machine learning could solve in a wide variety of industries and enable enterprises to adopt machine learning. It sounds more like a job for an enterprise salesman, not a Stanford research professor with over a hundred papers published in the field, but that would be the wrong conclusion.

A new artificial intelligence system can spot the tell-tale signs of skin cancer just as accurately as human doctors, say researchers, and the next step is to get the tech on a smartphone, so anyone can run a self-diagnosis. Once the system is refined further and becomes portable, it could give many more people the chance to get screened with minimal cost, and without having to wait for an appointment with a doctor to confirm the symptoms. The Stanford University researchers behind the deep learning system say the key to its success is an algorithm that enables it to apply what it knows from its existing database of skin cancer samples to pictures it hasn't seen before. "We made a very powerful machine learning algorithm that learns from data," says one of the team, Andre Esteva. "Instead of writing into computer code exactly what to look for, you let the algorithm figure it out."

Google's AI-powered health tech subsidiary, DeepMind Health, is planning to use a new technology loosely based on bitcoin to let hospitals, the NHS and eventually even patients track what happens to personal data in real-time. Dubbed "Verifiable Data Audit", the plan is to create a special digital ledger that automatically records every interaction with patient data in a cryptographically verifiable manner. This means any changes to, or access of, the data would be visible. DeepMind has been working in partnership with London's Royal Free Hospital to develop kidney monitoring software called Streams and has faced criticism from patient groups for what they claim are overly broad data sharing agreements. Critics fear that the data sharing has the potential to give DeepMind, and thus Google, too much power over the NHS.

Google's WaveNet can also synthesize realistic human speech, but it's quite computationally demanding and hard to use for real-world applications at this point. Baidu says it solved WaveNet's problem by using deep-learning techniques to convert text to phenomes, the smallest unit of speech. It then turns those phonemes into sounds using its speech synthesis network. The system converts the word "hello," for instance, into "(silence HH), (HH, EH), (EH, L), (L, OW), (OW, silence)" before the speech network pronounces it. Both steps rely on deep learning and don't need human input.