Google is testing a service that would use its search and artificial intelligence technology to analyze patient records for Ascension, the largest nonprofit health system in the U.S., according to documents about the efforts reviewed by Forbes. Called “‘Nightingale,” the Google-Ascension project indicates that Google’s push into health analysis is farther along than previously believed, even as the company has faced a growing backlash over health-related privacy concerns. 

Ascension said in a statement that all its work with Google complies with privacy law and is “underpinned by a robust data security and protection effort, which Google echoed in its own blog post later Monday, including that “patient data cannot and will not be combined with any Google consumer data. ”TheWall Street Journal first published details of the Ascension partnership earlier on Monday.

The project, in the works since at least February, involves Ascension moving patient records onto Google’s cloud serversand includes a search product that allows Ascension healthcare providers to see an “overview page” about their patients. The page includes complete patient information as well as notes about patient medical issues, test results and medications, including information from scanned documents, according to presentations viewed by Forbes. 

A source familiar with the project said that patients are not aware of Google’s access to their data, though patient privacy laws generally allow the sharing of patient data with third parties without notification if it is for purposes that “help it carry out its health care activities and functions.” Ascension employees have raised concerns internally, according to documents, about patient data privacy.

Down the line, Google’s AI technology could also help Ascension providers answer questions like, “What past history should you review about the patient?” “What are opportunities to intervene?” and “What are the risks of future outcomes?,” the documents indicate.  

Tech giants including Microsoft, Amazon, Google and Salesforce have been trying to carve out a slice of the trillion-dollar healthcare space. Improving the way caregivers use electronic health records has been a priority, as studies have shown that doctors spend more time on documentation than interacting with patients. Google’s Cloud unit, which now generates $8 billion in revenue a year, has made other inroads into healthcare. It counts the Mayo Clinic, McKesson and Kaiser Permanente as customers. 

Google and its parent company, Alphabet, have faced scrutiny for some of its past healthcare partnerships. This summer, a University of Chicago patient sued Google and the university, alleging that they improperly shared patient data for research that used artificial intelligence to predict future medical events. Google and the University of Chicago both denied any wrongdoing.  

In 2016, Alphabet’s artificial intelligence unit, Deepmind, came under fire for obtaining patient medical records from the U.K.’s National Health Services without proper patient consent. The company admitted mistakes and redrew its contracts.Privacy advocates were rankled further, however, last fall when Deepmind Health announced plans to merge with Google, reversing the company’s past pledges to keep its health work separate.  

Most recently, Google’s plans to acquire health wearables company Fitbit for $2.1 billion raised alarms with lawmakers, with Virgina Senator Mark Warner saying that the announcement “raises serious concerns” and calling for mandatory disclosures on how big tech companies use “sensitive data in healthcare products.”

Stacey Torvino, a bioethics expert and law professor at the University of Nevada, says that some privacy concerns around large tech companies handling medical records stem from the vast amount of other data that they store about people, including search and location history. Even if patient records were de-identified as outlined by the Health Insurance Portability and Accountability Act, HIPAA, the likes of Google and Facebook are uniquely capable of using other mined data to determine who a patient is.

“De-identification is getting to the point where it’s almost a myth” because of advances in big data analytics and machine learning, Torvino says.  

Google’s search service has rolled out in at least one Ascension facility each in Florida and Texas, and Google plans to release the product inAscension facilities in states including Michigan and Tennessee by the end of this year, the documents show. Missouri-based Ascension Health was founded in 1999 and is one of the largest Catholic health systems in the world, with 150 hospitals around the country and care centers in 20 states and Washington, D.C. 

David Feinberg, the leader of Google’s health unit, at a healthcare innovation conference in Las Vegas in early November, described a new Google product as “a search bar on top of your [electronic health records] that needs no training.” It appears that his comment refers to the new search tool being tested with Ascension.

“It’s understandable that people want to ask questions about our work with Ascension,” Google said in its statement. “We’re proud of the important work we’re doing as a cloud technology partner for healthcare companies.”

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Artificial intelligence (AI) is becoming a big part of our lives and is capable of doing the various task, generating convincing text is one of them. Though there are many concerns regarding it but tricking Google is prominent than any other concern. 

Numerous blogs, website and marketing spam content can be generated through AI along with keywords embedded in it. The content produced this way is more economical but would not be much meaningful. Still, the search engine would not be able to differentiate it from original content. 

Content marketing agency, Fractl showed how AI text generation can be implemented on a large scale for SEO (Search Engine Optimization) Business. 

Fractl partner, Kristin Tynski said that AI is capable of producing a massive amount of content which is hard for humans and search engines to discriminate. 

Grover, an open source tool developed by Allen Institute for Artificial Intelligence is used by Fractl to write blogs. Tynski said that though the company does not use AI to serve its client competitors are most likely to use it for their advantage. 

Tynski explained in a blog post on SEMrush that how almost 10 to 15 years ago a trend of article spinning started. By using tools, the words of original content were changed to make it look like a new piece of writing. Over time, Google and other search engines worked to differentiate the copied content with a lot of filters.

According to her, AI-generated text will be a lot better than spinning articles and will be capable of generating massive content in all possible niches. 

An SEO expert, Mike Blumenthal said that spammers will use these AI tools at a huge level, especially considering how massive content is produced at cheap cost. To generate traffic, many news merchants may create fake articles. 

Rowan Zeller, who created the Grover tool said that it is possible to detect AI-generated text, particularly at this stage. He along with his team already generated a system that was capable of identifying Grover-generated text, showing 92% accuracy. 

Zeller is of the view that AI-generated text has various linguistic and grammatical errors and thus can easily be identified. Instead of forming new phrases or nouns, it uses the same patterns. 

Even if a technology is developed to identify it, it has the potential to cause harm. It is not easy to integrate detectors in internet infrastructure. Also, the detectors can also make numerous mistakes and there is no guarantee of complete accuracy of detecting AI-generated content. 

Google did not comment on this issue but clarified that it always has and will continue fighting spam. 

Proxies like Siri and Alexa are used for web searches and search engines like Google have to show around 3 – 4 more relevant answers. Although, fewer answers may not be enough but they decrease the chances of spamming. 

AI text generation is improving rapidly and is expected to set benchmarks. A program that can create content like humans, it will not be a surprise to see it as an AI assistant. 

Tynski said that it is possible that in coming years, technology will be so advanced that it will be able to create the content of the level of humans. Once the human-accuracy achieved, it could give humans a tough time, as to why bother humans when there are systems to do tasks.

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Artificial intelligence (AI) applications are progressing by leaps and bounds today. But are we getting any closer to understanding human intelligence or replicating it in machines?

That is what the Center for Brains, Minds & Machines (CBMM) at the Massachusetts Institute of Technology (MIT) seeks to do.  CBMM is a multi-institutional NSF (National Science Foundation) Science and Technology Center headquartered at MIT, dedicated to developing a computationally based understanding of human intelligence and establishing an engineering practice based on that understanding.

CBMM has around 20 faculty members from institutions such as MIT, Harvard University, Stanford University and Rockefeller University. They are a combination of neuroscientists ,cognitive scientists and computer scientists. Neuroscientists  study the brain, cognitive scientists  look into the mind, and the computer scientists  work with machines. CBMM is about collaboration across these three disciplines.

OpenGov had the privilege of conversing with Prof Tomaso A. Poggio, Director at CBMM. He is also the Eugene McDermott Professor at the Department of Brain and Cognitive Sciences; and Member of the Computer Science and Artificial Intelligence Laboratory at MIT; and since 2000, a member of the faculty of the McGovern Institute for Brain Research.

Prof Poggio leads a program at MIT that counts amongst its alumni several of today’s AI leaders, including Demis Hassabis, cofounder of DeepMind, Amnon Shashua, cofounder of the autonomous driving tech company Mobileye, and Christof Koch, President and Chief Scientific Officer at the Allen Institute.

We asked Prof Poggio if there is any commonly agreed upon definition of intelligence and based on that, what would be the definition of artificial intelligence. He replied that people have yet to fully agree on a definition of intelligence.

One relatively standard definition is that the Turing Test, which essentially says if you see intelligence you know it. If machines cannot be distinguished from a human, then they are intelligent.

Prof Poggio pointed out that the interesting part which not many people realise is that the Turing test for intelligent machines is effectively a definition of human intelligence.

“I don’t think there is a definition of intelligence. I think that’s in a sense a meaningless question. Because there are so many different forms of intelligence. A computer that is better than humans at doing additions, is that intelligent? Super-intelligent?” he said.

“When people think about intelligence or intelligent computers, super-intelligent computers, what they really think about is human intelligence. It could be more memory, faster speed, but human intelligence.”

But if we are saying that a computer is intelligent, even if in a very narrow sense, does it matter how the system is arriving at that thought or action?

Prof Poggio said that one of the basic tenets of computing is that computing is in principle independent of hardware.

“You can have a computer which is made of silicon transistors, you can have a computer which is made of mechanical switches or even hydraulic switches or neuron cells. But they may not be different in what they compute.”

In reality, certain computations are faster in certain types of implementations and this depends on the hardware.

For instance, silicon computers are very good and very fast at particular computations that involve manipulating numbers, whereas our brain is not very good at it. And conversely, computers still struggle to recognise images or speech and cannot do so with low power consumption. They are just getting there though, primarily through data-driven deep learning techniques.

Deep learning attempts to mimic layers of “neurons” in the brain. Each layer combines a set of input values to produce an output value, which in turn is passed on to other layers downstream. Though the idea has been around for decades, explosion in data volumes and improved, cheaper computing power has enabled its widespread implementation during the last decade. And it has led to exponential improvement in areas such as image classification and facial recognition, resulting in the current deafening buzz around AI.

But still an AI system has to be trained with thousands or tens of thousands of images to distinguish a cat from a dog. We humans do not need to see 100,000 images to recognise a cat as a cat. a 3-year old can learn what is a cat from maybe three or four examples. So, we asked Prof Poggio about the potential and limitations of deep learning.

He responded that deep learning can lead to superhuman abilities in certain areas.

“As an example, I am able to find any papers I want with just a few keywords in Google search. This is much better than any human librarian could ever do,” he said. “There are certainly many other applications, where a combination of deep learning and having a lot of labelled data can lead to really superhuman abilities. But they are very specific.”

Today if we can define a task in a narrow enough way, such as recognising images (especially  those of a certain type), recognising speech in certain environments, recognising music, playing go, playing chess, driving a car, we can build the technology to perform it.

“What we don’t have is a system that can solve all of these things like a person, maybe none optimally, but all of them at the same time, and with not much training.”

In that case, we should not be worrying too much at the moment about AI taking over jobs, involving creativity, requiring perception?

Prof Poggio is not too worried at the moment. But he added, “At some point, we will make machines that are as intelligent as we are in some meaning that people will agree upon. I think it will take longer than people think, I think we are safe now.”

“One of the reasons is that I don’t think the problem of intelligence is one problem that you solve with one breakthrough. I think the problem of intelligence is many problems and will require many breakthroughs. Different aspects of human intelligence, like what I would say social intelligence, the ability to recognise what other people are thinking, whether they are happy or not, this is a part of the brain, and there is a separate part of the brain which is actually involved in the robotics part. And of course, they interact. But they are also separate.”

He referred to a recent article by Demis Hassabis, one of his former post-docs, saying that the next big breakthroughs in AI is likely to come from neuroscience. That is an idea Demis shares with Prof Poggio and that’s why collaboration is required between AI researchers and neuroscientists.

So, will we see general AI at some point of time, if not in the near future?

The answer is a bit complicated, Prof Poggio replied. He can see systems like Siri or Alexa slowly become more and more useful and intelligent. But if we are talking about having a system that is indistinguishable from a smart secretary in 15 or 20 years time, then the possibility would depend on what we mean by a smart secretary. If we want the smart secretary to be able to perceive human emotions, then it might not happen within the timeframe of another 15 to 20 years.

When asked if we are paying enough attention to the issues that might arise if general intelligence becomes a reality,Prof Poggio replied, “I think we are not paying enough attention to them”.

For instance, technology is going to take out a lot of jobs. It is already happening. In the US and in parts of Europe, we can see the rise of unrest and populism. But it is not so much immigrants stealing jobs, as it is machines stealing jobs.

“I can see that over the next 20 to 50 years, the jobs that will be safe would be jobs like scientists, engineers on one hand, and jobs like plumbers on the other hand. But in between, jobs like airline pilots, surgeons, tax advisors, financial consultants, they can all be potentially replaced. Replaced does not mean completely eliminated. It’s like the job of being a pilot. Basically today, there are one or two pilots babysitting a computer that is doing the flying. And the same is going to happen for a heart surgeon. Instead of hundred surgeons, you now need 10 (who are assisted by technology.”

New jobs will be created, but maybe not for the same people. If it happens too fast, it will be a problem. A big displacement would mean revolutions and wars. Here, Prof Poggio highlighted thatthe pace of disruption is critical.

Technological developments have caused societal disruption in the past. Horses were replaced by cars. But the disruption was slow enough that there weren’t too many revolutions.

“But this time it may happen faster, it may be more pervasive, I don’t think the tech industry is worrying enough about this. Because, it’s really a socio-political problem. If machines do their work that people do, you have to make sure that people who have lost their jobs are still being paid, can survive and are happy even without a job,” Prof Poggio said.

The fact that it’s not a matter of life and death tomorrow, makes it more difficult in a sense.

Prof Poggio cautioned, “It’s a relatively slow-moving train and by the time you notice it, it might be a bit too late.”

However, the problem can be solved. If people can agree and if there is the requisite political will. But we must take notice of the risks and start acting soon.

The post EXCLUSIVE – Can we engineer artificial intelligence without understanding human intelligence? appeared first on Artificial Intelligence.