Google could soon offer an ethics consultation service to companies building Artificial Technology solutions. According to a report in Wired, the search giant may soon offer the service to companies developing AI projects. The report suggests that Google might launch the service before the end of 2020 and initially, it would offer only advice on tasks such as spotting racial bias or creating guidelines to govern the AI projects. The American multinational may in the future also offer other services such as audits of AI systems, etc. 

Google’s own ethics-based controversies

Google’s plan to offer ethics advice to companies building AI solutions is being mocked even before it could be officially launched and there is a reason for that. Google doesn’t have a good track record when it comes to the company’s own ethics policy as it has been mired in several controversies in the past few years.

In 2015, the company had to apologise for a technical glitch in its Photos app that identified Black people as gorillas. In 2018, the company face backlash from its own employees after agreeing to develop surveillance software for the US defence department. 

The California-based company in 2018 reportedly tested a secret search engine it had developed for China, an authoritarian country where content is censored and monitored by the State. Company’s CEO Sundar Pichai had even testified for the same in the US Congress, where he admitted that Google was working on a search engine for China.

Following these controversies, the company issued a set of ethical principal for the use of its AI. Big companies pay Google for cloud computing services and as per reports some of them are already taking ethical help for the firm, which may have possibly prompted the Alphabet Inc-owned to dive into it. 

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Artificial Intelligence (AI) is fast gaining ground as the next big disruptor in the Media & Entertainment (M&E) industry. Over the last year, content creators across the globe have actively begun to adopt AI tools in order to enhance efficiencies, unlock revenue opportunities and deliver interactive viewing experiences. The uptake has already started in sports production and OTT distribution, with entertainment following close behind. This evolution has largely been made possible by the emergence of industry-specific AI solutions. 

It’s been a few years since content creators have had access to off-the-shelf AI solutions which focus on identification of facets like faces, objects, actions, emotions, transcripts etc. These have formed the building blocks for creating customized solutions which are tailor-made to serve the specific needs of content creators. The gap between AI technology and a solution that can actually solve M&E use cases is now getting bridged.  

Let’s take promo creation as an example. Promo producers and editors spend significant time in selecting the right content clips to build effective promos. We studied this process carefully and realized that AI can play a major role in streamlining this process. We decided to craft a media recognition engine to solve various use cases for content creators, including promo creation. But an engine that simply recognizes facets within the content was not good enough. In order to build context and deliver useful results, we needed to create a layer of ‘wisdom’ on top of recognition. And what better source of inspiration for wisdom could there be than the human brain? 

Mapping AI to human cognition 

Among the many theories on how the human brain works, our focus was on the multi-stream theory. Here’s an overview: 

As sensory signals, visual and auditory, arrive at the visual cortex of the brain, they branch out in three streams: Ventral, Dorsal and Limbic. 

The Ventral stream performs the ‘what’ function – it recognizes objects and entities in visual and auditory inputs 

The Dorsal stream performs the ‘where’ function – it processes the object’s spatial location in a context and creates a ‘map’ of discoveries 

The Limbic stream performs the ‘emotion’ function and the ‘gut feel prediction’ 

Researchers believe that these streams work together to help humans build context. So we replicated this concept in our AI engine, and got neural networks to behave like cortices of the human brain, recognizing various audio-video facets and then creating a map of these discoveries in a context. Here, context is a space-time combination that the engine identifies logically for each particular genre of content. In entertainment content, this will be a logical/meaningful scene with a set of clips that tell a small part of the bigger story. Finally, we added a probabilistic prediction dimension to guess what the user may be looking for even if it is not exactly the way a scene is described by the engine. The result was a media recognition engine that works well for both entertainments as well as sports content use cases.  

Solving real-life use cases 

For entertainment, the same content is recognized from different perspectives by a group of neural networks that identify objects, people, transcripts, logos, etc. The context is created in a different layer as logical scenes and clips, into which these facets are processed with probabilistic filtering. 

For sports, the main unit of the game forms the context like a point in tennis, a ball delivery in cricket or a pitch in baseball. The engine recognizes the content, processes it and makes sense of it in the context of this unit.  

Using this approach, we were able to create what we call ‘Machine Wisdom’, a patent-pending technology that helps solve specific use cases for the M&E industry. These include: 

Content Discovery and Search: The engine automatically identifies logical scenes and searchable clips in content and provides accurate, actionable, contextual and comprehensive sets of metadata. An editor can search for scenes that he/she is looking for using natural language. For example, the editor can simply search for strings like “Show me John Doe in a car chase”, “Bulls chasing John Doe on a bike”, “Jane Doe driving a Pontiac”; or even a signature dialogue. 

Sports: The engine automatically tracks sports action and creates highlights and packages in near real-time to engage with fans like never before. It helps sports producers cut down the time and effort involved in highlight creation by 60 per cent and enables OTT platforms to deliver high levels of interactivity and personalization for consumers. Additionally, the engine identifies storytelling graphics and events in footage along with game content to stitch together an end-to-end story in highlights, just like a human editor. 

Today, advances in AI and Machine Learning are opening up infinite possibilities for building innovative solutions to solve specific use cases. However, creativity and strategic thinking continue to differentiate humans from artificially intelligent entities. We have still not fully understood all the factors that make our intelligence unique, let alone been able to mimic them on machines. The good news is that AI solutions (like the one described above) are enabling creative teams to automate repetitive tasks, achieve faster time-to-market and free their personnel up to pursue far more creative pursuits. 

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A compounded annual growth rate (CAGR) of 40%: That’s how fast Accenture (NYSE: ACN)projects the market for artificial intelligence (AI) in healthcare will expand through 2021. You’ve probably read plenty of articles talking about how AI will transform the world as we know it. It’s not just hype — and definitely not when it comes to the healthcare industry.

Much of that growth will come from small companies that are privately held (for now). However, there are several public companies moving forward aggressively with AI initiatives focused on the medical sector that you can invest in now. Here’s why Google parent Alphabet(NASDAQ:GOOG) (NASDAQ:GOOGL), International Business Machines (NYSE:IBM), and NVIDIA (NASDAQ:NVDA) stand out as three of the top artificial intelligence stocks in healthcare.

Alphabet

Alphabet believes that “AI is poised to transform medicine, delivering new, assistive technologies that will empower doctors to better serve their patients.” The technology giant has multiple initiatives underway to help achieve that goal.

One major effort Alphabet is taking is using AI to help screen for diabetic retinopathy, an eye disease that’s the fastest-growing cause of preventable blindness in the world. The company developed a computer vision system that analyzes images of patients’ retinas and identifies diabetic retinopathy at similar rates as U.S. board-certified ophthalmologists. Another area Alphabet is focusing on is an AI system that can help pathologists detect breast cancer in lymph node biopsies.

There’s a lot more that the company hopes to be able to accomplish with AI in healthcare. For example, Alphabet has also partnered with the University of California San Francisco, the University of Chicago, and Stanford University to accurately predict medical events based on patterns in de-identified medical records.

Of course, most of Alphabet’s revenue and earnings continue to come from selling advertising, with especially strong growth recently in mobile search and YouTube ads. The company’s AI efforts remain only a small part of the value proposition for the stock. However, Alphabet’s AI initiatives in healthcare (and other areas) could become an increasingly important source of revenue in the future. The stock appears to be a good choice for investors hoping to ride the AI wave.

International Business Machines (IBM)

IBM doesn’t use the phrase “artificial intelligence” very often with its healthcare focus. Instead, the company prefers the term “cognitive health.” Whatever it’s called, though, Big Blue is a major player in advancing AI in the healthcare industry.

The company’s cognitive solutions unit, which includes IBM Watson Health, is already a significant source of revenue. In the first half of this year, cognitive solutions generated $8.6 billion in revenue, nearly one-quarter of IBM’s total revenue.

IBM Watson is being used in healthcare in several ways. It’s helping drugmakers identify new drug targets and additional indications for existing drugs. It’s helping government agencies deliver healthcare services more effectively. It’s also helping provide physicians with evidence-based treatment options for cancer patients. And that’s just the tip of the iceberg.

My Foolish colleague Rich Smith recently interviewed IBM Watson head Ed Harbour via email. Harbour noted that IBM has been researching and developing AI solutions for more than 50 years and led last year in AI-related patents held. Those certainly aren’t accomplishments to take lightly.

IBM stock hasn’t performed well lately, though. The company has experienced 21 quarters in a row of year-over-year revenue declines. However, I think IBM remains a good choice over the long run — and its dividend yield of nearly 4.2% is definitely attractive.

NVIDIA

You can’t talk about where AI is today without including NVIDIA in the conversation. The fast-growing chipmaker’s initial focus was on graphics processors for gaming systems, but researchers found out those processors were also great for AI algorithms. NVIDIA dove into AI deep learning headfirst in 2015 with the launch of Tegra X1 chip.

But is NVIDIA a stock to buy for healthcare AI? Absolutely. The company has developed an AI computing platform designed for the healthcare community. The NVIDIA Deep Learning GPU Training System (DIGITS) includes features such as neural networks to differentiate tumors from healthy tissue in digital images. NVIDIA DGX for Healthcare processes huge amounts of data, which allows researchers to develop more effective cancer drugs, conduct faster genomic analysis, and obtain more precise imaging results.

Many organizations currently use NVIDIA’s products to implement powerful AI solutions for healthcare. For example, personalized medical technology company HeartFlow uses NVIDIA’s chips in its computer vision algorithms that analyze medical imaging data to create a personalized 3D model of a patient’s heart and coronary arteries. Stanford University uses NVIDIA technology in an AI application that analyzes photos of skin lesions and classifies them as benign or malignant at accuracy levels achieved by dermatologists.

NVIDIA stock has soared over 160% in the last year and is up more than 1,000% over the last five years. As a result, the stock is priced to perfection, with shares trading at 42 times expected earnings. There could be some volatility in the months ahead, but the potential for AI in healthcare and practically every other industry is large enough that NVIDIA should continue to be a winner over the long run.

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