When medics apply radiotherapy to a cancer patient, they have to carefully determine which parts of the body should be exposed to radiation in order to kill the tumor while ensuring that as much healthy surrounding tissue as possible is preserved. "Clinicians will remain responsible for deciding radiotherapy treatment plans, but it is hoped that the segmentation process could be reduced from up to four hours to around an hour," explains DeepMind. It's currently drawing on 600,000 medical evidence reports and 1.5 million patient records and clinical trials to help doctors develop better treatment plans for cancer patients. After coming under fire earlier in the year when an app project appeared to provide DeepMind with free access to 1.6 million patients' records, the research outfit recently announced that it was helping to spot the early signs of visual degeneration by sifting through a million eye scans.

As an example, mobile network operators are increasing their investment in big data analytics and machine learning technologies as they transform into digital application developers and cognitive service providers. With a long history of handling huge datasets, and with their path now led by the IT ecosystem, mobile operators will devote more than $50 billion to big data analytics and machine learning technologies through 2021, according to the latest global market study by ABI Research. Machine learning can deliver benefits across telecom provider operations with financially-oriented applications - including fraud mitigation and revenue assurance - which currently make the most compelling use cases. Predictive machine learning applications for network performance optimization and real-time management will introduce more automation and efficient resource utilization.

In today's economy, we are seeing companies, business models, products, and processes undergoing major transformation. At the time, I felt that I was watching history in the making: The technology known as artificial intelligence (AI) was finally moving from the lab into the world. Second, the abundance of data being generated throughout the world today requires cognitive technology. Intelligence augmentation -- IA as opposed to AI -- will change how humans work together, make decisions, and manage organizations.

We live in a world where consumer attention span is getting shorter and shorter: 40 percent of people abandon a website that takes more than three seconds to load, and the average shopping cart is abandoned more than 68 percent of the time. Software platforms that drive ecommerce websites are creating visual search capabilities which allow consumers to upload an image and find similar/complementary products. The offline to online experience requires minimal steps to shop and purchase, providing a sense of autonomy to the consumer. Brands are creating more interactive shopping experiences to provide product recommendations based on natural conversation and cognitive data derived from AI.

Initiatives such as language translation and image, facial, activity and emotion recognition - are based on predictive analytics that get more accurate as the data behind them gets richer. In particular, the emergence of GPU-based computing can greatly accelerate neural network processing capabilities - and if more processing power is needed there are the vast cloud computing resources of Amazon, Microsoft, Google. "Taken together, deep learning software and parallel processing hardware now provide a powerful [machine intelligence] platform," the report said. Cloud business models: The emergence of machine learning business models based on the use of the cloud is the single biggest reason that the field is so energized today, the report said: "We are essentially seeing the merger of machine intelligence with cloud economics."

Many rural Ugandans don't have Internet access, and the radio is a central source of news -- and platform for citizens' opinions. Many rural Ugandans don't have Internet access, and the radio is a central source of news -- and platform for citizens' opinions. The inspiration for the tool came from projects that use social media to identify citizens' concerns -- for instance, what concerns people have about an immunization drive, or how often they suffer power outages. But at the Global Pulse lab in Kampala, Uganda, social media analysis wouldn't work, says lab manager Paula Hidalgo-Sanchis -- especially if the U.N. wanted to listen to rural voices.

From there, it won't be long before we begin to wonder how we ever lived without artificially intelligent financial advisors implementing our own personal monetary policy. U.S. financial literacy levels are unacceptably low, and the widespread availability of artificially intelligent money-management tools won't change that. By enabling us to make simple, direct decisions while taking care of the rest, artificially intelligent financial advisors will decrease the prevalence of consumer mistakes and prompt improvement in our overall financial health.I'm actually a perfect example of this point. And while this figures to make things physically easier, the process still won't be simple.

New York-based Calrifai -- backed by investors including Google Ventures, Lux Capital, and NVidia -- entered the R/GA accelerator this year, after raising $10M in Series A in Q2'15. BI, Sales & CRM: Applications here include voice analytics to extract information from calls, automated customer response solutions, business data analytics, and sales targeting. To name a few, Palo Alto-based Mariana raised $2M in seed money from investors including Blumberg Capital; London-based True AI, previously seed funded by Entrepreneur First, entered the Microsoft Ventures Accelerator in Q3'16; another UK-based startup, Ripjar, raised funds from Winton Ventures in Q2'16. Three startups in the private sector using AI in e-commerce raised funding rounds this year: Reflektion raised $18M in Q1'16 from investors including Intel Capital, Battery Ventures, and Marc Benioff; ViSenze raised $10.5M in Series B from investors including Rakuten Ventures, Enspire Capital, and Phillip Private Equity; India-based Staqu raised angel funds in Q2'16.

Over the next 15 years, AI technologies will continue to make inroads in nearly every area of our lives, from education to entertainment, health care to security. "Now is the time to consider the design, ethical, and policy challenges that AI technologies raise," said Grosz. The report investigates eight areas of human activity in which AI technologies are already affecting urban life and will be even more pervasive by 2030: transportation, home/service robots, health care, education, entertainment, low-resource communities, public safety and security, employment, and the workplace. Some of the biggest challenges in the next 15 years will be creating safe and reliable hardware for autonomous cars and health care robots; gaining public trust for AI systems, especially in low-resource communities; and overcoming fears that the technology will marginalize humans in the workplace.