Improving AI Systems with Human Feedback and no Heartburn

Humans play an indispensable role in many modern AI-enabled services – not just as consumers of the service, but as the actual intelligence behind the artificial intelligence. From news portals to e-commerce websites, it is people’s ratings, clicks, and other interactions which provide a teaching signal used by the underlying intelligent systems to learn. While these human-in-the-loop systems improve through user interaction over time, they must also provide enough short-term benefit to people to be helpful. Continue reading “Improving AI Systems with Human Feedback and no Heartburn”

Getting Linked In to Data Science with Dr. Igor Perisic

Dr. Igor Perisic – Chief Data Officer

Episode 11, February 7, 2018

Getting Linked In to Data Science with Dr. Igor Perisic

Big data is a big deal, and if you follow the popular technical press, you’ll have heard all the metaphors: data is the new oil, the new bacon, the new currency, the new electricity. It’s even been called the new black. While data may not actually be any of these things, we can say this: in today’s networked world, data is increasingly valuable, and it is essential to research, both basic and applied. Continue reading “Getting Linked In to Data Science with Dr. Igor Perisic”

The new podcast is here, and just in time for your commute. Don’t miss a chance to hear from Ruth Pickering about how @DoYewno is helping others uncover the undiscovered faster with AI and neural networking. https://blog.outsellinc.com/uncovering-the-undiscovered-faster-with-ai-and-neural-networking-e02eb28dc1b3 …

The new podcast is here, and just in time for your commute. Don’t miss a chance to hear from Ruth Pickering about how is helping others uncover the undiscovered faster with AI and neural networking. https://blog.outsellinc.com/uncovering-the-undiscovered-faster-with-ai-and-neural-networking-e02eb28dc1b3 …

How the practice of design enhances artificial intelligence

Artificial Intelligence (AI) systems can perform amazing feats of problem-solving. But no matter how accurate AI solutions are, they won’t be relevant, insightful and adopted by people without great design work.

The practice of design is about problem solving. It starts long before the visual look and feel is created and continues long afterward. It creates a vital connection between humans and machines that allows AI systems to perform at their best.

In this article, I’ll focus on discrete cognitive machine tools and systems built for specific tasks, rather than Artificial General Intelligence. Continue reading “How the practice of design enhances artificial intelligence”

Can AI Win the War Against Fake News?

Developers are working on tools that can help spot suspect stories and call them out, but it may be the beginning of an automated arms race.

“Testing a demo version of the AdVerif.ai, the AI recognized the Onion as satire (which has fooled many people in the past). Breitbart stories were classified as “unreliable, right, political, bias,” while Cosmopolitan was considered “left.” It could tell when a Twitter account was using a logo but the links weren’t associated with the brand it was portraying. AdVerif.ai not only found that a story on Natural News with the headline “Evidence points to Bitcoin being an NSA-engineered psyop to roll out one-world digital currency” was from a blacklisted site, but identified it as a fake news story popping up on other blacklisted sites without any references in legitimate news organizations.”

Tracking a Descent to Savagery with the Wolfram Language: Plotting Sentiment Analysis in Lord of the Flies

Computation is no longer the preserve of science and engineering, so I thought I would share a simple computational literary analysis that I did with my daughter.

Shell Lord of the Flies

 

Hannah’s favorite book is Lord of the Flies by William Golding, and as part of a project she was doing, she wanted to find some quantitative information to support to her critique. Continue reading “Tracking a Descent to Savagery with the Wolfram Language: Plotting Sentiment Analysis in Lord of the Flies”

Fact-Checking Software Detects Genetic Errors in Cancer Research Publications

In early October, two scientists shared a software program that detects incorrect gene sequences in already published research experiments. Using the program, the duo identified experimental flaws in more than 60 papers within cancer research alone. Scientists Jennifer Byrne and Cyril Labbé combined their expertise in cancer-research and computer-science, to introduce the software “Seek &…
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‘Contextual bandit’ breakthrough enables deeper personalization

Microsoft senior researcher Miro Dudík.

News portals that simultaneously personalize every part of the landing page for every visitor and mobile health apps that adaptively tweak every part of an exercise regimen to maximize the benefit of every user are becoming plausible due to an advance in a type of interactive machine learning that my team will describe at the Annual Conference on Neural Information Processing Systems running December 4-9 in Long Beach, California. Continue reading “‘Contextual bandit’ breakthrough enables deeper personalization”

Tech giants are using open source frameworks to dominate the AI community


Tech giants such as Google and Baidu spent from $20 billion to $30 billion on AI last year, according to the recent McKinsey Global Institute Study. Out of this wealth, 90 percent fueled R&D and deployment, and 10 percent went toward AI acquisitions.

Research plays a crucial role in the AI movement, and tech giants have to do everything in their power to seem viable to the AI community. AI is mostly based on research advances and state-of-the-art technology, which is advancing very quickly. Therefore, there is no business need to make closed infrastructure solutions, because within a few months everything will be totally different.

In such a situation, the only winning strategy for tech giants is to offer open source solutions to attract members of the AI community and eventually become part of the AI community themselves. This is a relatively new model in the tech industry. Continue reading “Tech giants are using open source frameworks to dominate the AI community”

paper published in arXiv by researchers from Stanford describes a deep neural network that can look at a patient’s records and estimate the chance of mortality in the next three to 12 months. The team found that this serves as a good way to identify patients who could benefit from palliative care. Importantly, the algorithm also creates reports to explain its predictions to doctors.

AI could help solve the world’s healthcare problems at scale


In a world with limited doctors, emerging diseases and superbugs, and sharply rising healthcare costs, how can we successfully tackle healthcare problems at scale?
This is just one of the critical challenges India’s explosive startup community hopes to solve by implementing AI in new and innovative ways to serve the needs of 1.324 billion citizens. This is a feat that carries huge implications for the US and other healthcare ecosystems around the globe.

To understand how dire the situation is, it’s worth considering India’s health paradox. The country’s deep demographic dividend — which occurs when the majority of a country’s population consists of working-age individuals — is driving rapid and unprecedented growth, but it is also a ticking time bomb. With an average age of 27, India has one of the youngest and most educated populations in the world. Since 1991, this phenomenon has fueled approximately 7% annual growth, produced new goods and services, and reduced dependents in the economy.

But in order to keep reaping the benefits of this dividend, India’s young population needs to have access to quality nutrition and healthcare. In addition, as the dividend declines (as we are witnessing in China), the country will need new infrastructure in place to care for its aging population. And unfortunately, the infrastructure that is necessary doesn’t exist today.

The doctor-to-patient ratio in India is one of the worst in the world, with just 0.2 doctors for every 1,000 Indians (for comparison, there are 1.1 doctors for every 1,000 Americans in the US). Modern medical facilities — and as a result, doctors — are heavily concentrated in urban areas. In addition to heart disease, cancer, and is so bad, for instance, that it was deemed equivalent to smoking 44 cigarettes per day.

The fundamental reason behind India’s healthcare issues is resource scarcity. India needs more medical facilities and more medical expertise, and both of these require time and billions of dollars to develop. But such resources are not easily obtainable, so we must consider other ways to dramatically increase access to existing resources in an effective and inexpensive way.

This is where AI has the potential to reshape India’s healthcare problem. Manu Rekhi, Managing Director of Inventus, says, “Indian AI platform companies are building upon two decades of India’s IT industry expertise. They are supercharging how software and human intelligence can partner to create new human-in-the-loop AI systems for global markets as well as the bottom of the pyramid.”

Indeed, a number of Indian startups have implemented deep AI expertise to move the needle on specific health conditions and disease. In some cases, these companies offer technology and distribution opportunities, which attract Fortune 500 giants to partner with them both for the India market and globally.

One such company is Tricog Health, a startup handpicked by GE’s healthcare accelerator program for its cloud-based cardiac diagnosis platform. Coronary heart disease is increasingly prevalent in India, having escalated from causing 26% of adult deaths in 2003 to 32% in 2013. Tricog increases access to cardiac care across 340 cities in 23 states, including in some of the most remote locations in India. The company’s platform collects physiological data and ECGs from medical devices in the field then uses specialized AI to process the data in real-time and provide a diagnosis to the cardiologist.The cardiologist then reviews and recommends next steps to the GP or nurse in the field instantaneously using the Tricog mobile app. By using Tricog’s AI engine, a few specialists can diagnose over 20,000 patients.

Another startup, Bengaluru-based Aindra Systems, is using AI to tackle cervical cancer, which is the second most common cancer in Indian women between the ages of 15 and 60. In fact, India represents a whopping one-third of the total global incidences of cervical cancer. Aindra’s solution can detect cervical cancer in its early stages and measurably increase the odds of survival. The company increases the productivity of pathologists who screen cervical cancer samples, who otherwise typically need to manually examine each sample and flag cases with a high cancer probability to an oncologist for further review.

Adarsh Natarajan, Founder and CEO of Aindra Systems, says “Our vision is to implement mass cervical cancer screening using AI, and help the 330 million Indian women in the at-risk age bracket. With early detection, up to 90% of deaths can be avoided with appropriate treatment. Aindra’s computational pathology platform includes an affordable and portable, ‘point-of-care’ cervical cancer screening device to automate deep learning analysis and bring down the screening time significantly to help detect cancer at an early stage.”

The AI boom in healthcare is just starting, and the up-and-coming list of players is endless. Niramai is working on early detection of breast cancer. Ten3T is providing remote health monitoring services via AI to detect anomalies and alert the doctor. HealthifyMe, a Bangalore startup, is working on lifestyle diseases like obesity, hypertension, and diabetes. With its AI-enabled nutrition coach, Ria, HealthifyMe brings the best of elite nutrition expertise with AI in the loop.

And of course, global corporate leaders like Google bring their capabilities to India as well. Google recently partnered with Aravind Eye Hospitals to use image recognition algorithms to detect early signs of diabetic retinopathy, an eye disease that can cause blindness in diabetics if not treated early. Aravind Eye Hospitals is the largest eye care group in the world, having treated 32 million patients and performed 4 million surgeries. They have provided 128,000 retinal images to Google that have been invaluable for the application of AI to detect diabetic retinopathy in 415 million at-risk diabetic patients worldwide.

With a bevy of solutions on the rise, India is poised to leapfrog some of the key barriers of conventional healthcare, which of course has profound implications for healthcare delivery in other countries, including the US. With rising costs and unfavorable government policies, an increasing number of people are priced out of access. The burden on emergency rooms across the country is increasing as more people are unable to afford preventative care at primary care centers. AI-assisted technologies could reduce the costs in the US using the same mechanism — affordably scaling access to millions of people.

These startup-driven innovations and global platforms are just the tip of the iceberg. AI can ultimately become a force multiplier in bringing preventative healthcare facilities to anyone and everyone, rather than just urban or affluent communities. As you’ll often hear AI experts say, “more data beats better algorithms.” In other words, simpler algorithms only need a larger training dataset to generate accurate, valuable predictions for both payers and providers. With 1.3 billion citizens, India has the potential to provide the vast amounts of data needed to improve the accuracy and precision of our algorithms and empower both startups and large companies to help solve healthcare problems around the world.

Pranav Deshpande works with Startup Bridge at Stanford, an annual conference held in December at Stanford where top technology innovators from India and Silicon Valley build strategic partnerships to innovate for the world.  

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