Dr. Roshini Beenukumar

Is superintelligence the inevitable next step in evolution? by Dr. Roshini Beenukumar, @roshiniBR

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Experts predict that by 2050, there is a 50% probability that AIs which will match the intelligence of an average adult human. It is not a long wait, isn’t it?


Last week, my husband and I finally sat down to plan our summer vacation. After spending two full hours on 10 different tour websites, we ended up being completely overwhelmed by the options; all-inclusive vs half-board, city vs seaside or sea vs land. 


Decision making is getting more challenging and time-consuming day-by-day. With growing amounts of data and information, there is an increasing need for intelligent systems that could help us make our daily decisions. Several applications use AI to help customers with their decision making, for example, Amazon recommending you the smoothie maker that you always wanted but never bought or Netflix recommending you a new crime series to watch because you are a crime series fan. 

The rise of the “bots”

The AI trend is here and it is going to grow in the coming years. Investors are backing more AI startups than ever. In the first quarter of 2016, there were over 140 equity deals to startups focused on AI, according to CB insights. The spotlight is currently on chatbots and voice assistants. Bot startups like Growbot, Angel.ai, X.ai, Digit, Meekan, Viv and others help their customers with scheduling meetings, tracking employee accomplishments, tracking spending as well as finding the right products and services. This means that, the next time I plan a vacation I could just go to Angel.ai and ask the bot to find me the right vacation package!


As an AI enthusiast, there isn’t a shred of doubt in my mind that AI systems are here to make our lives easier. However, it is hard to ignore issues raised by prominent thought leaders in the field like Stephen Hawking and Elon Musk on the future of AI. Once machine intelligence surpasses human intelligence, will it become an existential threat to the human civilization? 

What happens when AIs start doing AI research?

“Machine intelligence is the last invention that humanity will ever need to make.” Oxford philosopher and leading AI thinker Nick Bostrom, TED, March 2015. 

The answer lies in the concept of intelligence explosion, says Daniel Dewey, an AI researcher from the University of Oxford. The thought experiment goes like this. Imagine that we have created a machine that is more capable than today’s computers. This machine is given the task to improve its current capacity. This leads to a very large and rapid increase in the abilities of these machines. This is called intelligence explosion. To prevent unwanted consequences resulting from such superintelligent AIs, it is absolutely essential that the AI research community collaborate to carefully steer the evolution of artificial intelligence.

Evolution of intelligence on an exponential scale

“It is hard to think of any problem that a superintelligence could not either solve or at least help us solve. Disease, poverty, environmental destruction, unnecessary suffering of all kinds: these are things that a superintelligence equipped with advanced nanotechnology would be capable of eliminating.” – Ray Kurzweil, The Singularity Is Near 

AI is among the top three technologies that is expected to grow on an exponential scale, says Ray Kurzweil, founder of Singularity University. As of now, humans have conquered the lowest calibre of AI called the “weak AI” that specializes in one area, like Google’s AlphaGo beating the world champion in the game GO. The next step in the AI Revolution is a “human-level AI” or “strong AI” with a general mental capacity matching that of a human being. The final step in the evolutionary ladder is postulated to be an ASI or “artificial superintelligence” which is not only smarter than humans but also self-improving. 

A group of AI researchers at companies and leading research institutions around the world are making significant strides in the field of AI. The fruits of their work can significantly change the way we live on this planet. Experts predict that by 2050, there is a 50% probability that AIs which will match the intelligence of an average adult human. It is not a long wait, isn’t it?  

Author
Roshini Beenukumar

Dr. Beenukumar is a molecular biologist turned science writer. During her PhD, she studied how cancer cells behave the way they do by exploring the humble yeast. Currently, she works as a freelance science/technical writer in the Life Sciences industry . She enjoys communicating science to the public and discussing new ideas in the interface of medicine and technology. She spends her spare time getting lost in a book or in nature.

House MD vs Doctor #AI- Who will turn out to be the better by @RoshiniBR


House MD vs Doctor AI- Who will turn out to be the better diagnostician?


Do a google search for “all-inclusive beach holiday” and all you see for the next weeks are advertisements for all-inclusive holidays following the virtual you. Google knows you better than your family or friends- creepy but true! Whether or not we realize it, we have made the decision to donate our data to the virtual world.

Can we put our data to better use – to improve healthcare on a scale unimaginable a decade or two ago?


Artificially intelligent systems in healthcare

AI systems feed on big data. Big data is nothing but a massive amount of data, the sheer size of which makes data analysis a challenge. AI systems are being developed to analyze and recognize meaningful patterns out of this complex data. Of particular significance is deep learning, a branch of AI which attempts to mimic the thinking part of the human brain. Several startups are now attempting to put AI, particularly deep learning to meaningful use in healthcare.

Diagnosing diseases: Clinical diagnosis is essentially a data problem, says founder of Enlitic, a machine learning startup focusing on data-driven medicine. They aim to revolutionize clinical diagnosis by helping physicians to automatically screen for specific diseases using their proprietary technology. Another startup, Deep Genomics, is approaching diagnosis from a genomic perspective. They capitalize on machine learning technology to link genetic variations to diseases. As a cherry on top, a study published as recent as last week reported that AI-enabled automated cancer detection perform as well as approaches that require costly clinician input.

“We think that its no longer necessary for humans to spend time reviewing text reports to determine if cancer is present or not”- author of the study, Shaun Grannis M.D., M.S.


Educating Patients: IBM is teaming up with the American Cancer Society to create an adviser for cancer patients, powered by IBM’s Watson health – health division of its AI brain. It would be designed to provide cancer patients or their caregivers with personalized guidance based on the patient’s particular disease stage and treatment. Watson would sift through countless digital sources like health websites and draw relevant and trustworthy information catering to individual needs. 

Improving clinical trials: Many clinical trials fail because patients fail to take their medications. AiCure is addressing this issue using AI to monitor medication adherence thereby increasing trial success.  They directly monitor patients using artificial intelligence on mobile devices via an app which collects real-time dosing data on a centralized and cloud-based platform. IBM Watson is trying to solve another issue facing clinical trials – enrolling patients. Watson would sift through clinical trial data at Mayo Clinic and in public databases, such as ClinicalTrials.gov. and match patients more accurately and consistently to clinical trial options.

Using Watson’s cognitive computing capabilities, Mayo Clinic can consistently offer more cutting-edge medical options to patients and conclude trials faster”  – Mike Rhodin, senior vice president, IBM Watson Group.


Accelerating drug discovery: Drug discovery is a lengthy, complex, and costly process, rooted with a high degree of uncertainty that a drug will actually succeed. Several machine learning startups are trying to solve this long-standing predicament in pharmaceutical drug development. Berg Health combines systems biology with its proprietary artificial intelligence machine learning analytics program to bring down the time for cancer drug development to almost half. Atomwise’s drug discovery AI platform “learns like a human chemist” using deep learning algorithms and supercomputers.

“Simulating billions of virtual medicines to find potential commercial candidates in weeks? That’s what we call truly transformative.”- Matt Ocko, Managing Partner of Data Collective, a venture firm backing Atomwise


Reducing hospital costs: Readmissions are costly for hospitals as payers are reluctant to reimburse preventable readmissions. Hindsait, an artificial intelligence technology provider, helps identify patients who are nearing the end of their stay and suggests whether keeping them might in fact be more cost effective than risking readmission. It does so by applying AI based data analysis to large health datasets.

These and many more upcoming developments in cognitive computing in healthcare leaves us with the thought – will AI systems perform better diagnosis than out best diagnosticians. Google’s AI program, AlphaGo beating Go world champion and IBM’s Watson beating humans in Jeopardy seems to suggest so.

Note: Interestingly, there were many recent publications that influenced the direction of this article. Application of AI technologies in healthcare is growing with new collaborations being made at this very moment. Interesting times indeed for all those who influence and follow these developments. 

Author
Roshini Beenukumar

Dr. Beenukumar is a molecular biologist turned science writer. During her PhD, she studied how cancer cells behave the way they do by exploring the humble yeast. Currently, she works as a freelance science/technical writer in the Life Sciences industry . She enjoys communicating science to the public and discussing new ideas in the interface of medicine and technology. She spends her spare time getting lost in a book or in nature.
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