Left image in each pair, a traditional parse tree view, but the vertical length of each branch represents embedding distance. Right images: PCA projection of context embeddings, where color shows deviation from expected distance.

An example of how AI improves patient care is Amsterdam UMC's partnership with SAS. The project was able to clinically diagnose patients with colorectal liver cancer, the third most common cancer worldwide, using Computer vision and predictive analysis. Previously, this process required manual examination which was time-consuming and subjective to the radiologist. Automating this process has increased accuracy and saved time to ensure patient survival. Whether it's image analysis to detect cancer or other diseases immediately, predicting the number of patients to ensure the right number of doctors and hospital beds are available or using natural language processing (NLP) to understand lengthy patients reports – the potential for technological enhancement in healthcare is colossal.

The ability to forecast events at scale, given a set of variables, is something most companies would find useful. So Amazon is aiming to make prediction more accessible with a fully managed service called Forecast that uses AI and machine learning to deliver highly accurate forecasts. As Amazon explained in a press release, Forecast -- which is based on the same technology the Seattle company uses to anticipate demand for hundreds of millions of products every day -- can be used to build precise forecasts for virtually any business condition, including product demand and sales, infrastructure requirements, energy needs, and staffing levels. It automatically provisions the necessary cloud infrastructure and processes data, building custom AI models hosted on AWS without requiring an ounce of machine learning experience on the part of developers. Amazon says the API or a console allows the average person to build custom machine learning models in less than five clicks and achieve accuracy levels that would normally take months in as little as a few hours.

Robert Chang, a Stanford ophthalmologist, normally stays busy prescribing drops and performing eye surgery. But a few years ago, he decided to jump on a hot new trend in his field: artificial intelligence. Doctors like Chang often rely on eye imaging to track the development of conditions like glaucoma. With enough scans, he reasoned, he might find patterns that could help him better interpret test results. That is, if he could get his hands on enough data.

Tel Aviv is the city with the highest number of startups per capita in the world, according to the 2018 Global Startup Ecosystem report -- more than 6,000, of which 18 are unicorns. The city's tech cluster, dubbed Silicon Wadi, is home to more than 100 venture capital funds, plus hundreds of accelerators and co-working places. "Tel Aviv is transitioning from startup nation to scale-up nation," says Eyal Gura, co-founder of Zebra Medical Vision. Amit Gilon, an investor at Kaedan Capital VC fund, agrees – adding that Israel is not just about successful B2B companies anymore, such as Checkpoint, Nice and Amdocs, but also about "big B2C success stories like Playtika, Wix, Fiverr and others". Founded in 2015, Arbe has built a 4D ultra-high-resolution imaging radar for cars.

This article is part 1 of a series sharing the initial results and directions of Omdena's AI challenge on PTSD treatment with 34 engineers and enthusiasts collaborating. Millions of people suffer from PTSD around the world due to various kinds of traumatic events. Professional help is difficult to find when needed in particular areas, which makes it sometimes impossible for the patient to overcome the trauma they live in. This is why Christoph von Toggenburg reached out to Omdena for leverage AI and community collaboration. Before we dive in deeper let's first understand what PTSD is and how professional psychiatrists treat it.

As revolutionary as blockchain technology is, it is no secret that a number of flaws remain present. Notably, a number of existing blockchains are still unable to reach consensus on how to improve speed and scalability, without compromising on security and decentralization. While the technology does offer a number of key benefits, the underlying network is arguably not fit for purpose. With that being said, we seek to explore whether emerging technologies such as artificial intelligence (AI) can enhance the capabilities of existing blockchain frameworks. Whether it's with respect to manufacturing processes, the customer experience, agriculture or health care – the benefits of AI are practically endless.

The financial services industry has seen a great deal of disruption from digital-based alternatives. Many of these challengers use advanced technology and expanded data sets to offer apps that provide financial solutions at a lower cost, with less friction and greater personalization than traditional bank or credit union offerings. Toronto-based startup Flybits believes that the best way to compete in the future is not just by developing innovative products and services, but by becoming the repository of choice for data in addition to money. "I definitely see that banks are in a perfect position, if they innovate right, to be the perfect data vaults for the future – managing the privacy and also the data of their customers," says Hossein Rahnama, CEO and Co-Founder of Flybits, in an exclusive interview for Banking Transformed, a new podcast from Jim Marous and The Financial Brand. "Using AI and machine learning, there is the potential to build a'data marketplace' for banks, fintechs and other data providers to partner and build more services together."

Machine learning has grown to have a significant impact on our daily lives: From Amazon's home assistant Alexa collecting and analyzing information to anticipate our needs, or Facebook suggesting who we should friend, to applications protecting us from credit card fraud and improving online shopping experiences. Organizations want their data to do the heavy lifting for them, driven by the desire to save on costs, improve consistency and streamline operations. While ML technologies were previously perceived as an excessive expenditure, today they are seen as an investment in the business' future and a competitive revenue driver. In order to stay competitive and successful, organizations have to invest in the right technologies and intelligently use the skills and data systems that they already have. The following three tips will help enterprises evaluate ML benefits and investments and make the most of the technology they already have.

Facial-recognition software is increasingly being used to track individuals without their permission.Credit: David McNew/AFP/Getty China wants to be the world's leader in artificial intelligence (AI) by 2030. The United States has a strategic plan to retain the top spot, and, by some measures, already leads in influential papers, hardware and AI talent. Other wealthy nations are also jockeying for a place in the world AI league. A kind of AI arms race is under way, and governments and corporations are pouring eye-watering sums into research and development. The prize, and it's a big one, is that AI is forecast to add around US$15 trillion to the world economy by 2030 -- more than four times the 2017 gross domestic product of Germany.