Check out the "Deep Learning models" sessions at the Artificial Intelligence Conference in San Francisco, September 4-7, 2018. One of the key findings of a survey we released earlier this year (How Companies are Putting AI to Work through Deep Learning) was that the leading reason holding companies back from incorporating deep learning was their lack of access to skilled people. One-fifth of respondents pointed to a skills gap as one of the reasons they haven't integrated deep learning, and at the time of the survey, 75% of respondents indicated their company had some combination of internal and external training programs to address this issue. We've continued to monitor interest in topics relevant to building AI products and systems, specifically areas that also warrant investment in skills development. In this post, I'll share results of related studies we've conducted.

Everyone who's done a Deep Learning project knows how long it takes to train a single model not to mention optimizing it. As a current student, I don't have access to any GPUs which sometimes leads to frustration because I can spend most of my time waiting for the model to see all the training batches. As you might have heard, GPUs can considerably decrease training time when used for Deep Learning projects. In a nutshell, a GPU is a graphics processing unit that is now integrated in most laptops to efficiently manipulate computer graphics and image processing. It has a highly parallel structure that allows it to make more computations in parallel than the usual CPU (central processing unit). This is why GPUs have become the popular kid when it comes to neural networks (NN).

Artificial Intelligence is improving day by day and making improvements in a myriad of industries including healthcare. AI is gradually learning the way to diagnose a disease like any human physician. In the future, they might replace doctors in hospitals near you. The latest development has taken place in London where Google's DeepMind subsidiary, UCL, and Moorfields Eye Hospital have incorporated deep learning to develop a software that helps in diagnosing a multitude of common eye diseases from 3D scans and recommend some treatment for the patients. The development has taken place because of a multiyear collaboration between the three institutes.

DeepMind's artificial intelligence can now spot key signs of eye disease as well as the world's top doctors. Anonymous diagnostic data from almost 15,000 NHS patients was used to help the AI learn how to spot 10 key features of eye disease from complex optical coherence tomography (OCT) retinal scans. An OCT scan uses light rather than X-rays or ultrasound to generate 3D images of the back of the eye, revealing abnormalities that may be signs of disease. The system has the potential to prevent irreversible sight loss by ensuring that patients with the most serious eye conditions receive early treatment. DeepMind's new system was developed alongside scientists at Moorfields, University College London.

England's Grand National run at Aintree is gruelling. It has 30 fences, two with open ditches, in a distance of 2.25 miles that's completed twice. AI has just moved up the field in the eHealth equivalent. An AI project at London's Moorfields Eye Hospital with Google's DeepMind has accurately diagnosed eye conditions from scans. As ophthalmologists' workloads and their complexities increase, diagnostic imaging is expanding faster than specialists can interpret the results.

Microsoft's chatbot Xiaoice does a lot more than other bots. She has presented the weather on live TV and now even composed a book of poems. Xiaoice has written 12 million of poems in fact. In a paper on arXiv, researchers from Microsoft, National Taiwan University, and the University of Montreal explained that all text Xiaoice wrote is inspired by images. "Given an image, we first extract a few keywords representing objects and sentiments perceived from the image. These keywords are then expanded to related ones based on their associations in human written poems. Finally, verses are generated gradually from the keywords using recurrent neural networks trained on existing poems," according to the paper's abstract.

DeepMind and its partners in the research, London's Moorfields Eye Hospital and the University College London Institute of Ophthalmology, said they plan prospective clinical trials of the technology in 2019. If those trials are successful, DeepMind said it would seek to create a regulator-approved product that Moorfields could roll out across the U.K. It said the product would be free for an initial five-year period. The software would be the first time a DeepMind AI algorithm using machine learning has ended up in a healthcare product. Alphabet has several initiatives aimed at using artificial intelligence to improve healthcare. Earlier this year, Verily, an Alphabet company that says its goal is to extend human lifespans, teamed up with AI experts from Alphabet's Google, to develop an algorithm that could spot a range of cardiovascular issues from a different kind of retinal image.

If you want to learn how to start building professional, career-boosting mobile apps and use Machine Learning to take things to the next level, then this course is for you. The Complete iOS Machine Learning Masterclass is the only course that you need for machine learning on iOS. Machine Learning is a fast-growing field that is revolutionizing many industries with tech giants like Google and IBM taking the lead. In this course, you'll use the most cutting-edge iOS Machine Learning technology stacks to add a layer of intelligence and polish to your mobile apps. We're approaching a new era where only apps and games that are considered "smart" will survive.

If you want to learn how to start building professional, career-boosting mobile apps and use Machine Learning to take things to the next level, then this course is for you. The Complete iOS Machine Learning Masterclass is the only course that you need for machine learning on iOS. Machine Learning is a fast-growing field that is revolutionizing many industries with tech giants like Google and IBM taking the lead. In this course, you'll use the most cutting-edge iOS Machine Learning technology stacks to add a layer of intelligence and polish to your mobile apps. We're approaching a new era where only apps and games that are considered "smart" will survive.

But a new system designed by Google DeepMind and British doctors goes a crucial step further: It can show users how it reached its conclusions. A study published Monday in Nature Medicine reports that the DeepMind system can identify dozens of diseases and point out the portions of optical coherence tomography scans that it relies upon to make its diagnoses. That's a crucial factor in validating the safety and efficacy of AI technologies being developed for use in diagnosing or recommending treatments for a broad range of diseases, from cancer to neurological and vision problems. The paper states that the system made the right referral recommendation in more than 94 percent of cases based on a review of historic patient scans at Moorfields Eye Hospital in London and performed as good as, or better than, top eye specialists who examined the same scans. Experts said that level of accuracy is impressive on such an open-ended query.