A technology incubator in the company, called Jigsaw--formerly known as Google Ideas--says it intends to spot and remove digital harassment with an automated program called Conversation AI. So if Conversation AI or similar tools make it easier and more efficient to exercise such control, it's a reminder that "solving" the abuse problem, whether through human or automated means, requires moving away from maximal inclusivity as the highest ideal online. The Wild West nature of those sites will become only more apparent if tools like Conversation AI make moderated sites function even better. While Jigsaw efforts like Project Shield aim to provide defenses for politically sensitive websites, Conversation AI makes it easier to filter out unwanted speech--but the question is, unwanted by whom?

While robots and computers will probably never completely replace doctors and nurses, machine learning/deep learning and AI are transforming the healthcare industry, improving outcomes, and changing the way doctors think about providing care. Machine learning is improving diagnostics, predicting outcomes, and just beginning to scratch the surface of personalized care. Lumiata has developed predictive analytics tools that can discover accurate insights and make predictions related to symptoms, diagnoses, procedures, and medications for individual patients or patient groups. The Care Trio team has developed a three-pronged approach that helps doctors devise and understand the best care protocols for cancer patients.

It was in this same dingy diner in April 1993 that three young electrical engineers--Malachowsky, Curtis Priem and Nvidia's current CEO, Jen-Hsun Huang--started a company devoted to making specialized chips that would generate faster and more realistic graphics for video games. "We've been investing in a lot of startups applying deep learning to many areas, and every single one effectively comes in building on Nvidia's platform," says Marc Andreessen of venture capital firm Andreessen Horowitz. Starting in 2006, Nvidia released a programming tool kit called CUDA that allowed coders to easily program each individual pixel on a screen. From his bedroom, Krizhevsky had plugged 1.2 million images into a deep learning neural network powered by two Nvidia GeForce gaming cards.

Dubbed as Amazon AI, the new service offers powerful AI capabilities such as image analysis, text to speech conversion, and natural language processing. On the analytics front, Amazon is adding a new interactive, serverless query service called Amazon Athena that can be used to retrieve data stored in Amazon S3. With this, customers can run and manage workloads in the cloud, seamlessly from existing VMware tools. Extending Lambda to connected devices, AWS has announced AWS Greengrass – an embedded Lambda compute environment that can be installed in IoT devices and hubs.

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."

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.