Daily Archives: November 25, 2019

We could have gone for a stock image of a Shakespare text, but this is far more interesting.

SHAKESPEARE MAY BE one of the most famous Brits to ever live, but there's an ongoing suspicion that all the work credited to him may not actually be his. Now AI is on the case, and it seems to have confirmed one of the most widely believed theories: that the bard had a co-author on Henry VIII.

As long suspected, that co-author seems to be John Fletcher. That theory has been around since 1850, but the methods of testing it have been human-based text analyses. Czech researcher Petr Plech turned to AI to look for patterns that humans might miss, and although the results confirmed the 169-year-old theory, there were a couple of twists worthy of the man himself.

By feeding his machine learning algorithm eight plays - four by Shakespeare and four by Fletcher - the AI's verdict was that Henry VIII was a near 50/50 production between the two men.

And although most scenes are either one man or the other, it looks like there was mixed authorship going on in places suggesting they actively collaborated. Act 3, Scene 2 is a belter for this: Fletcher seems to starts it off, before the two work together at line 2,081, and then Shakespeare takes over completely at 2,200.

Okay, but does it actively prove that Fletcher was the guy rather than an unknown other? It looks that way, as Plech tried feeding the AI three works of another playwright rumoured to be involved: Philip Massinger (not to be confused with former Leeds United striker Phil Masinga, who wouldn't be born for another three centuries). Plech's verdict? "The participation of Philip Massinger is rather unlikely." Take that, Phil.

Of course, while AI can make a good fist of spotting these patterns, it's always possible that humans can muddy the waters. If Shakespeare and Fletcher were collaborating closely on works, it's possible they would try and match each other's styles, for example.

Plus there's also the ongoing question about Shakespeare's sole authorship anyway, with Christopher Marlowe getting an extremely belated co-author credit in three of Shakespeare's plays just three years ago - something that really stress tests "better late than never" as proverbs go.

See more here:

AI confirms Shakespeare's suspected co-author - The INQUIRER

Do you know who you are? If so, do you direct your own actions? These are two questions that we ask ourselves when someone asks us about our identities. In the west, individualism is valued. We like to think that we have agency in our own actions. We like to think that our identities are not affected by the world. We are distinct. We are unique.

The history of identity theory

There are two classes of identity theory: mind-brain identity theory (from philosophy) and social identity theory (from psychology). Mind-brain identity theory, originated in the 1930s with psychologist E. G. Boring, also known as type physicalism states that mental events can be grouped into types that correlate with physical events in the brain. Over time, psychologists Feigel and Smart distinguished sensations from brain processes but asserted that they refer to the same physical phenomenon. Mind-brain identity theory makes us think that identity is an individual construct and an independent construct.

In contrast, social identity theory, originated in the 1970s and 80s with psychologists Henri Tajfel and John Turner, states that ones self-concept comes from membership in social groups such as family, school, and community. In this construct, membership mobility, competition and creativity affect the individuals identity. Social identity theory makes us think that we have less agency than we do since our identity is formed from interactions in social groups and environments.

Chances are our identities are developed and maintained by a mix of these two identity theories. Our brains network determines how we respond to social situations. The reflections of our identities in social situations help our brain to recognize and form judgments about who we really are. Our true identities are formed from the interaction between the mechanisms defined in these theories.

Brain augmentation and identity theory

Brain augmentation is not a new concept. From the 1950s, ever since Robert G. Heath demonstrated that you can use electrical stimulation to treat patients with mental illnesses, there were waves of physicians following suit to treat their patients with brain stimulation. From that point onwards, the US military experimented with mind control techniques for the use of the military. Eventually, in the 2000s, military programs developed implants on animals brains via a Micro-Electro-Mechanical Systems that could effectively control the animals. Cyborg Insects and Cyborg Sharks were the results of such research. Its important to note that brain stimulation in the form of ECT (Electroconvulsive Therapy) and recently, TMS (Transcranial Magnetic Stimulation) are being used widely for treatment of mood disorders.

Its not hard to see the origins of brain augmentation in Mind-brain identity theory. In a sense, brain augmentation realized the core concepts of mind-brain identity theory: mental events can relate to physical events in the brain. By identifying these events, they can be monitored, controlled or even modified to elicit the desired physical response. This is essentially brain augmentation.

Ethical Implications of Brain Augmentation

Its not hard to see the ethical implications of brain augmentation. With the power of artificial intelligence, if the brain can be controlled, then any responses from the individual can also be controlled. This is when we start to lose our human agency.

Innovation often contain a certain element of risk. Managing that risk is critical to help with the advancement of innovation. There are three areas of focus to setting the right ethical boundaries of brain augmentation:

Neuralink and Elon Musk

When Elon Musk released his white paper showcasing the research from the Neuralink project, the element that received most attention was the Neural Lace implant. The implant would theoretically grow inside the brain with the brain and become an AI layer on top of the brain to enhance the brains activities.

In his presentation, Elon Musk demonstrated how we can converge with AI with the possibilities of an implant that can be safe to insert and maintain. He alluded to alleviating symptoms of mental illnesses and illnesses affecting the brains activities.

After the presentation, scientists debated the possibilities of curing diseases with this type of device. Artificial Intelligence enthusiasts contemplated possibilities of humans enhanced by artificial intelligence. Essentially, to adapt to the age of artificial intelligence, we may need an implant to help us enhance our abilities. This is as evolutionists like to call human-machine evolution. We will evolve with the help of our machine counterparts.

Whats scientific and whats real?

Even though, extensive research and progress has been made in the area of brain augmentation with artificial intelligence, we still have a long way to gobefore a viable implant is tested and can be used by humans to treat certain diseases. Without the first proof of concept, we will have to wait and see. In the mean time, ethical considerations must be elicited and thoroughly discussed.

What new research did was to give us a vision of future possibilities that raises many questions in our lives. It makes us think about human agency, ethical conflicts, security concerns, privacy concerns, and health hazards.

Rather than focusing on the small steps of innovation we take to get there, we need some time and resources to process these implications first.

Conclusion

The future is as uncertain as ever. The timeline is uncertain as well. However, its a real accomplishment just to be able to see the vision of human-machine evolution. In the age of artificial intelligence, this vision may finally be realizable. Do we want this vision to be realized? Have we thought about the implications? These are the questions well be asking in the new year, and undoubtedly, the years ahead.

Read more:

AI, Brain Augmentation And Our Identities - Forbes

Computers can beat humans at sophisticated tasks like the game Go, but can they also drive a car, ... [+] speak languages, play soccer, and perform a myriad of other tasks like humans? Heres what AI can learn from biology.

In building the worlds first airplane at the dawn of the 20th century, the Wright Brothers took inspiration from the insightful movements of birds. They observed and reverse-engineered aspects of the wing in nature, which in turn helped them make important discoveries about aerodynamics and propulsion.

Similarly, to build machines that think, why not seek inspiration from the three pounds of matter that operates between our ears? Geoffrey Hinton, a pioneer of artificial intelligence and winner of the Turing Award, seemed to agree: I have always been convinced that the only way to get artificial intelligence to work is to do the computation in a way similar to the human brain.

So whats next for artificial intelligence (AI)? Could the next wave of AI be inspired by rapid advances in biology? Can the tools for understanding brain circuits at the molecular level lead us to a higher, systems-level understanding of how the human mind works?

The answer is likely yes, and the flow of ideas between learning about biological systems and developing artificial ones has actually been going on for decades.

First of all, what does biology have to do with machine learning? It may surprise you to learn that much of the progress in machine learning stems from insights from psychology and neuroscience. Reinforcement learning (RL) one of the three paradigms of machine learning (the two others being supervised learning and unsupervised learning) originates from animal and cognitive neuroscience studies going all the way back to the 1940s. RL is central to some of todays most advanced AI systems, such as AlphaGo, the widely-publicized AI agent developed by leading AI company Google DeepMind. AlphaGo defeated the worlds top-ranked players at Go, a Chinese board game that comprises more board combinations than there are atoms in the universe.

Despite AlphaGos superhuman performance in the game of Go, its human opponent still possesses far more general intelligence. He can drive a car, speak languages, play soccer, and perform a myriad of other tasks in any kind of environment. Current AI systems are largely incapable of using the knowledge learned to play poker and transfer it to another task, like playing a game of Cluedo. These systems are focused on a single, narrow environment and require vast amounts of data, and training time. And still, they make simple errors like mistaking a chihuahua for a muffin!

The internet meme asks, Is this a chihuahua or a muffin? But developing an AI system to solve this ... [+] seemingly simple human task is not that easy.

Similar to child learning, reinforcement learning is based on the AI systems interaction with its environment. It performs actions that seek to maximize the reward and avoid punishments. Driven by curiosity, children are active learners that simultaneously explore their surrounding environment and predict their actions outcomes, allowing them to build mental models to think causally. If, for example, they decide to push the red car, spill the flower vase, or crawl the other direction, they will adjust their behavior based on the outcomes of their actions.

Children experience different environments in which they find themselves navigating and interacting with various contexts and objects dispositions, often in unusual manners. Just as child brain development could inspire the development of AI systems, the RL agents learning mechanisms are parallel to the brains learning mechanisms driven by the release of dopamine - a neurotransmitter key to the central nervous system - which trains the prefrontal cortex in response to experiences and thus shapes stimulus-response associations as well as outcome predictions.

Biology is one of the most promising beneficiaries of artificial intelligence. From investigating mind-boggling combinations of genetic mutations that contribute to obesity to examining the byzantine pathways that lead some cells to go haywire and produce cancer, biology produces an inordinate amount of complex, convoluted data. But the information contained within these datasets often offers valuable insights that could be used to improve our health.

In the field of synthetic biology, where engineers seek to rewire living organisms and program them with new functions, many scientists are harnessing AI to design more effective experiments, analyze their data, and use it to create groundbreaking therapeutics. I recently highlighted five companies that are integrating machine learning with synthetic biology to pave the way for better science and better engineering.

Artificial general intelligence (AGI) describes a system that is capable of mimicking human-like abilities such as planning, reasoning, or emotions. Billions of dollars have been invested in this exciting and potentially lucrative area, leading some to make claims like data is the new oil.

Among the many companies working on general artificial intelligence are Googles DeepMind, the Swiss AI lab IDSIA, Nnaisense, Vicarious, Maluuba, the OpenCog Foundation, Adaptive AI, LIDA, and Numenta. Organizations such as the Machine Intelligence Research Institute and OpenAI also state AGI as their main goal. One of the goals of the international Human Brain Project is to simulate the human brain.

Despite a growing body of talent, tools, and high-quality data needed to achieve AGI, we still have a long way to go to achieve this.

Today, AI techniques such as Machine Learning (ML) are ubiquitous in our society, reaching from healthcare and manufacturing to transportation and warfare but are qualified as narrow AI. They indeed process and learn powerfully large amounts of data to identify insightful and informative patterns for a single task, such as predicting airline ticket prices, distinguishing dogs from cats in images, and generating your movie recommendations on Netflix.

In biology, AI is also changing your health care. It is generating more and better drug candidates (Insitro), sequencing your genome (Veritas Genetics), and detecting your cancer earlier and earlier (Freenom).

As humans, we are able to quickly acquire knowledge in one context and generalize it to another environment across novel multiple situations and tasks, which would allow us to develop more efficient self-driving car systems as they need to perform many tasks on the road concurrently. In AI research, this concept is known as transfer learning. It assists an AI system in learning from just a few examples instead of the millions that traditional computing systems usually need to build a system that learns from first principles, abstracts the acquired knowledge, and generalizes it to new tasks and contexts.

To produce more advanced AI, we need to better understand the brains inner workings that allow us to portray the world around us. There is a synergistic mission between understanding biological intelligence and creating an artificial one, seeking inspiration from our brain might help us bridge that gap.

Acknowledgment: Thank you to Louis N. Andre for additional research and reporting in this article. Im the founder of SynBioBeta, and some of the companies that I write about including some named in this article are sponsors of the SynBioBeta conference (click here for a full list of sponsors).

Link:

Can Synthetic Biology Inspire The Next Wave Of AI? - Forbes

Lung cancer is a common and often aggressive form of cancer. As it is difficult for doctors to detect it early on, people with lung cancer need to receive the best, most targeted therapy in order to make a positive outlook more likely. Immunotherapy is an option, but how can doctors know who will benefit?

According to the National Cancer Institute, lung and bronchus cancer is the second most widespread type of cancer among people in the United States, accounting for 12.9% of all new cancer cases.

This form of cancer often has no noticeable symptoms in its early stages, which can mean that doctors are unable to detect it at first. This means that the outlook following treatment may not be as good as for other forms of cancer.

To ensure the most favorable outcomes for people with lung cancer, healthcare professionals must choose the best type of treatment for each individual. This, however, can be tricky, since it is often hard to tell which person will benefit the most from a particular treatment.

It can also be difficult for a doctor to determine how beneficial newer types of treatments, such as immunotherapy, will be for an individual. Unlike chemotherapy, which involves using specific drugs to attack and destroy cancer cells, immunotherapy works by boosting a person's immune response against cancer tumors.

Now, a team led by researchers from Case Western Reserve University in Cleveland, OH in collaboration with scientists from six other institutions has developed a new artificial intelligence (AI) model. The model allows healthcare practitioners to find which people with lung cancer would benefit the most from immunotherapy.

The investigators explain their method and report their findings in a study paper that features in the journal Cancer Immunology Research.

"Even though immunotherapy has changed the entire ecosystem of cancer," explains study co-author Anant Madabhushi, "it also remains extremely expensive about $200,000 per patient, per year.

"That's part of the financial toxicity that comes along with cancer and results in about 42% of all newly diagnosed cancer patients losing their life savings within a year of diagnosis," he adds. Madabhushi also notes that the new tool he and his colleagues are working on may help doctors and patients decide which therapy suits them best and avoid unnecessary expenses.

Madabhushi explains that he and his colleagues developed their new model based on recent findings that identified the signs that show which cancerous tumors are responding to treatment.

In a previous study, the investigators found that while doctors have typically thought that tumor size was a good indicator of whether or not a therapeutic approach is working, looking at this characteristic alone can be deceptive.

Instead, says Madabhushi, "[w]e have found that textural change is a better predictor of whether the therapy is working."

"Sometimes, for example, the nodule may appear larger after therapy because of another reason, say a broken vessel inside the tumor but the therapy is actually working," he explains. "Now, we have a way of knowing that."

To develop the new AI model, the team first used data from computed tomography (CT) scans from 50 people with lung cancer. This allowed them to set up a mathematical method able to identify any changes in size and texture taking place in the tumor after exposure to two to three cycles of immunotherapy.

The method found patterns indicating that particular changes in tumors were associated with a positive response to the immunotherapy treatment, as well as with higher patient survival rates.

This study highlighted once again that those lung cancer tumors that show the most noticeable changes in texture are also the ones who best respond to immunotherapy.

"This is a demonstration of the fundamental value of the program, that our machine-learning model could predict response in patients treated with different immune checkpoint inhibitors. We are dealing with a fundamental biological principle."

Study co-author Prateek Prasanna

Earlier this year, co-author Prateek Prasanna received an American Society of Clinical Oncology 2019 Conquer Cancer Foundation Merit Award for research associated with this study.

Going forward, the team is planning to further test their AI method on more CT scans from other sites, and from people treated with different immunotherapy agents.

Here is the original post:

Lung cancer: AI shows who will benefit from immunotherapy - Medical News Today

Hike, Indias AI-led unicorn is working at the intersection of AI and art to innovate and build magical experiences for the Indian market. With AI, Hike believes technology can finally begin to wrap itself around people, and together with art, bring a bit of romantic value into these incredibly personalised experiences.

Using advancements in the field of AI together with a unique art style, Hike recently launched HikeMoji, the first and only made-in-India hyperlocal avatar. Hike believes in the online world one can go beyond the constraints of the offline world, hence one of its principles with HikeMoji is that its 60 percent of what a user is and 40 percent of what they would love to be.

AI plays a key role in bringing HikeMoji to life, using advanced Computer Vision and deep neural networks.

Hike is also one of the only tech companies that also has a huge focus on art which enables it to create an incredible hyperlocal experience. With HikeMoji Stickers, hikers can supercharge expression beyond the keyboard. With over 1,000 hairstyles, facial features, bindis, local clothing, nose pins and more to choose from, users can make their HikeMoji truly their own. To start with, users get over 100 exclusive HikeMoji Stickers of themselves in any of the seven regional languages available, in addition to English and Hindi.

Talking about how the artwork was key to making it work, Kavin adds, People need to feel that this is them, so weve ensured that the HikeMojis feel relatable but theyre also aspirational. Theres a local flavour to the avatar and all the 1000+ customisations that come with it. Users also get over 100 exclusive HikeMoji stickers that only they can use to start with.

HikeMoji uses hyperlocal elements to reflect a relatable ethnic identity to the emojis. Whats unique to Hike is that the artwork across has an essential Indian feel. From colours, design schemes and personalised nuances, it all comes to life with hand-drawn elements to maintain high quality.

As Indias AI-led unicorn startup, Hike takes its growth and innovation pretty seriously. The company is one of the top three patent filers in the field of IT, with 66 patents filed in the year 2017-2018. Hike has also launched the Hike Patent Program, which not only incentivises Hike employees with rewards and grants but also lends legal and market guidance to prospective patent filers. The company is also offering rewards and grants ranging up to 60,000 per inventor, depending on the impact of the idea. The company has also presented papers in IJCAI, ECIR and has represented in global platforms including Tensor Flow and the World AI Show.

Excited to find out more? Well, now you can! Hike is bringing its exclusive event, AI First: Innovating at the intersection of Art and AI to Bengaluru. This is where attendees can get a ringside view of its innovation story and strategy,

The event will focus on the areas they have worked on so far, and the road ahead. You will hear from Kavin Bharti Mittal (Founder and CEO, Hike), Dr. Ankur Narang (VP of AI and Data Technologies), and Anshuman Misra (VP of Operations), on all things AI and Art. It will be hosted on November 30, 2019 at the Ritz Carlton Bengaluru.

Entry to the event is by invite only and is non-transferrable, think you should be on the list? Register below and get a chance to score an invite and be part of this exclusive event.

Register for an invite here.

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AI and art magic: Learn how Hike is innovating at the intersection of AI and art at their exclusive AI First - YourStory

Aging Analytics Agency

People over 50 are the fastest growing demographic group worldwide. This creates both opportunities and challenges for the global economy and healthcare systems. The Longevity Industry, which provides products and services for those aged over 50 is becoming a multi-trillion-dollar industry. There are currently 260 companies, 250 investors, 10 non-profits, and 10 research labs in the Longevity Industry in the UK alone. In the next decade, Longevity policies enacted by governments, and changes in the financial industry will transform society. Achieving small but practical results in Longevity distributed at scale will have enormous and multiplicative effects on society. Extending the functional lifespan of humans by just one year will decrease suffering for tens of millions of people and will improve the quality of life for billions of people.

Despite having more potential to increase healthy Longevity in the short term than any other sector, AI for Longevity is an underrepresented sector in the Longevity Industry. AI holds enormous potential to rapidly accelerate the implementation of longevity research and development. To address this, Ageing Research at King's (ARK) in collaboration with the Biogerontology Research Foundation, Deep Knowledge Ventures (at which I am a Managing Partner), and other organizations have established the Longevity AI Consortium at Kings College London. The Consortium will use King's world-leading advances in genetics, AI and ageing research to develop advanced personalized consumer and patient care. The Consortium will help accelerate advances in Longevity using a unique academic-industry focus on preventive and personalized physical, mental and financial health. The establishment of the AI Longevity Consortium and AI Longevity Accelerator at Kings College has the potential to help make the UK the worlds leading AI for Longevity Hub and creates an opportunity for huge advances in Longevity research which will benefit people all around the world.

Over the next few years, the Longevity AI Consortium plans to expand to include centers in Switzerland, Israel, Singapore and the US. This collaborative effort involves sophisticated methods for translating advanced AI for Longevity solutions, along with the development of advanced frameworks and technologies including novel applications of life data for insurance companies, pension funds, healthcare companies, and government bodies. These new technologies and instruments at the forefront of the rising Longevity Financial Industry provide practical applications of preventive medicine and precision health.

Sophisticated Analytical Frameworks for Precision Medicine and Longevity Diagnostics, Prognostics ... [+] and Therapeutic Benchmarking.

The Longevity AI Consortium will serve as a leading R&D hub and industry-academic hotspot for advanced AI-driven personalized preventive diagnostics, prognostics and therapeutics. This represents a paradigm shift from treatment to prevention and a new frontier - from precision medicine to precision health, enabling the UK to become the leading global hub for the application of AI to Longevity and Precision Health. The Longevity AI Consortium plans to dedicate resources to R&D in other niches where the science is ahead of the funding: e.g. microbiome diagnostics and therapeutics, recent advancements and innovations in advanced cosmetics in particular. The Consortium aims to identify novel longevity and healthy ageing biomarkers, accelerate diagnosis of age-related health decline, develop personalised physical, mental and financial health to better implement and promote effective healthy lifestyles for longevity, such as modifying patterns in sleep, nutrition, physical activity, environmental exposure and financial planning.

The Longevity AI Consortium will use sophisticated and multidimensional analytical frameworks developed by Aging Analytics Agency to perform industry benchmarking in precision health and personalized preventive medicine clinics in order to construct the ideal diagnostic, prognostic and therapeutic pipeline using the most advanced market-ready methods and technologies. The Consortium will develop a comprehensive cloud computing platform to enable the development of minimum viable and most comprehensive panels of biomarkers of aging, creating an ecosystem that incentivizes the participation of doctors, clinics, data providers, AI companies and corporate partners, and which enables efficient and streamlined commercialization and clinical implementation of both validated and experimental biomarkers of ageing as a framework for the extension of national Healthy Longevity in the UK.

This mind map provides an overview of the Longevity Industry UK Landscape including 260 companies, ... [+] 250 investors, 10 non-profits, and 10 research labs. (Image source Aging Analytics Agency).

Kings College London is the logical choice of location for the first Longevity AI Consortium, due to their unique combination of resources, departments and technologies for both AI and Longevity. Kings is also an ideal location for the AI Consortium because it has dedicated divisions and resources both for AI and for Longevity. Furthermore, being located in London, it is in an ideal physical location to engage in cross-sector and industry-academic collaboration. The AI Longevity Consortium is currently designing a complementary AI Consortium for Financial Wellness which will utilize financial and behavioural data to develop products and services to enable UK citizens to maintain financial stability, social activity and psychological well-being across extended periods of Healthy Longevity.

Currently there are only three centers in the world actively working to apply AI to precision health for healthy Longevity. These include the US based Buck Institute for Research on Aging, US based Y Combinator, and the US based AI Precision Health Institute (AI-PHI) at the University of Hawaii Cancer Center. Only the AI-PHI has actually succeeded in practice. Now that the Longevity AI Consortium has been established at Kings College London, the UK can immediately leverage its existing resources, including its very well-developed AI industry and its reputation for extremely strong industry, academic, and governmental cooperative initiatives, to become the number one global hub for the application of AI to Longevity and Precision Health.

There are currently four major AI Centres for Healthcare in major industry, academic and metropolitan hubs in the UK, but none of them have a specific focus on Longevity, precision health and preventive medicine. While these centres serve as a precedent and proof-of-concept for the viability of an AI Centre for Longevity, they do not adequately address the need for a leading AI for Longevity R&D nexus within the UK capable of developing leading solutions, products and services that leverage the power of AI to implement practical, real-world product, services and solutions to extend citizens Healthy Longevity.

Sophisticated Analytical Frameworks for Precision Medicine and Longevity Diagnostics, Prognostics ... [+] and Therapeutic Benchmarking

2020 and Beyond

In 2020, following the completion of several key development milestones, the Longevity AI Consortium plans to launch an AI Longevity Accelerator Program that will serve as a much-needed bridge between startups focusing on AI for Longevity research and drug development and major UK investors. While the UKs AI and Longevity industry ecosystems are very developed individually, the number of longevity startups utilizing AI in a major way for their internal R&D is comparatively small. The potential impact presented by the synergy of these two sectors is huge. The UK government has heavily prioritized the separate sectors of AI and Longevity, including both sectors in the top 4 Industrial Strategy Grand Challenges. However, by uniting the AI and Longevity verticals in unique and convergent ways the UK could leverage the nations strengths in these industries to their maximum potential.

AI Longevity Accelerator at Kings College aims to develop an infrastructure to promote increased investments and developments in the AI for Longevity sector to provide AI for Longevity startups with relevant levels of funding. Startups selected for inclusion in the accelerator will also receive mentorship and incubation resources, and will gain access to a global network of experts in the areas of scientific R&D, business development and investment relations. The AI Accelerator will also provide startups with the tools necessary to grow, expand and evolve following their time in the Accelerator, and will equip them with the skills required to develop further through later-stage venture capital and government grants. Longevity companies that prove capable of achieving tangible results may become the next Googles, and investment firms that invest in those companies may become the SoftBanks and Vision Funds of tomorrow. AI holds enormous potential to rapidly accelerate the implementation of Longevity research and development. The establishment of the AI Longevity Consortium and AI Longevity Accelerator at Kings College has the potential to make the UK the worlds leading AI for Longevity Hub and creates an opportunity for huge advances in Longevity research which will benefit billions of people all around the world.

Click the box below for more information on the Longevity AI Consortium.

Click the box below for information on the AI Longevity Accelerator.

Click the box below for in-depth information about the Global Longevity Industry and explore a book that I co-authored with my colleague Dmitry Kaminskiy: Longevity Industry 1.0 - Defining the Biggest and Most Complex Industry in Human History.

Continued here:

Leveraging AI To Accelerate Precision Health For Longevity - Forbes

BERKELEY, Calif., Nov. 25, 2019 /PRNewswire/ --Rad AI, a startup transforming radiology with the latest advances in technology, today announces its company launch and a $4 million seed round led by Gradient Ventures, Google's AI-focused venture fund. Investors UP2398, Precursor Ventures, GMO Venture Partners, Array Ventures, Hike Ventures, Fifty Years VC and various angels also participated in this round.

Founder Dr. Jeff Chang, the youngest radiologist and second youngest doctor on record in the US, was troubled by high error rates, radiologist burnout, and rising imaging demand despite a worsening shortage of US radiologists, so he decided to pursue graduate work in machine learning to identify ways that AI could help. After he met serial entrepreneur Doktor Gurson, they created Rad AI in 2018 at the intersection of radiology and AI. Built by radiologists, for radiologists, Rad AI is transforming the field of radiology with the inside perspective as its driving force.

"Radiology is facing severe pressures that range from falling reimbursements to market consolidation. There is also a radiologist shortage that is exacerbated by rising imaging volumes nationwide. We help radiology groups significantly increase productivity, while reducing radiologist burnout and improving report accuracy. By working closely with radiologists, we can make a positive impact on patient care," said Dr. Chang.

Rad AI uses state-of-the-art machine learning to automate repetitive tasks for radiologists so they have more time to focus on what matters: accurate and timely diagnosis for patients. The first product automatically generates the impression section of radiology reports, customized specifically to the preferred language of each radiologist. Initial customers haveshown significant reduction in radiologist burnout, error rates, and turnaround time improving radiologists' well-being and patient care.

Rad AI's current partners include Greensboro Radiology, Medford Radiology, Einstein Healthcare Network, and Bay Imaging Consultants, one of largest private radiology groups in the United States, as well as other radiology groups that have yet to be announced. Product rollouts have demonstrated an average of 20% time savings on the interpretation of CTs and 15% time savings on radiographs translating into an hour a day saved for each radiologist.

With this new capital, Rad AI will build out its engineering team and expand the rollout of its first product to more radiology groups and customers.

Zachary Bratun-Glennon, Partner at Gradient Ventures, added, "The team at Rad AI is uniquely suited to apply innovative technology to this field, with strong radiology and AI experts and firsthand knowledge of this market. It's exciting to see the quantitative benefits and positive feedback from their radiology customers, and we're looking forward to the impact of their future products."

Rad AI is participating in the upcoming RSNA 2019 Annual Meetingin Chicago later this month, located in the AI Showcase at #10514B (North Hall, Level 2). The team will also present and demo at the Nvidia, AWS, and M*Modal (3M) booths - #10939, #10942, and #6513.

For more information on Rad AI, please visit: https://www.radai.com/

About Rad AIFounded in 2018 by Doktor Gurson and Dr. Jeff Chang, Rad AI uses machine learning to transform the practice of radiology. Its AI products are designed by radiologists, for radiologists. By streamlining existing workflow and automating repetitive manual tasks, Rad AI increases daily productivity while reducing radiologist burnout. In addition, Rad AI provides more consistent radiology reports for ordering clinicians, and higher accuracy for the patients it serves.The company has raised over $4 million in funding and is based in Berkeley, CA. For more information, visit: https://www.radai.com/

About GradientGradient Ventures is Google's AI-focused venture fund - investing in and connecting early stage startups with Google's resources, innovation, and technical leadership in artificial intelligence. The fund focuses on helping founders navigate the challenges in developing new technology products, allowing companies to take advantage of the latest advances in machine learning, so that great ideas can come to life. Gradient was founded in 2017 and is based in Palo Alto, California. For more information, visit http://www.Gradient.com.

Media Contact:press@radai.com

View original content:http://www.prnewswire.com/news-releases/rad-ai-launches-with-4-million-in-funding-to-build-innovations-in-radiology-300964646.html

SOURCE Rad AI

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Rad AI Launches With $4 Million In Funding To Build Innovations In Radiology - P&T Community

DUBLIN--(BUSINESS WIRE)--The "Artificial Intelligence in Precision Health" book from Elsevier Science and Technology has been added to ResearchAndMarkets.com's offering.

Artificial Intelligence in Precision Health: From Concept to Applications provides a readily available resource to understand artificial intelligence and its real time applications in precision medicine in practice. Written by experts from different countries and with diverse background, the content encompasses accessible knowledge easily understandable for non-specialists in computer sciences. The book discusses topics such as cognitive computing and emotional intelligence, big data analysis, clinical decision support systems, deep learning, personal omics, digital health, predictive models, prediction of epidemics, drug discovery, precision nutrition and fitness. Additionally, there is a section dedicated to discuss and analyze AI products related to precision healthcare already available.

Key Topics Covered:

Section 1: Artificial Intelligence Technologies 1. Interpretable Artificial Intelligence: Addressing the Adoption Gap in Medicine 2. Artificial Intelligence methods in computer-aided diagnostic tools and decision support analytics for clinical informatics 3. Deep learning in Precision Medicine 4. Machine learning systems and precision medicine: a conceptual and experimental approach to single individual statistics 5. Machine learning in digital health, recent trends and on-going challenges 6. Data Mining to Transform Clinical and Translational Research Findings into Precision Health

Section II: Applications and Precision Systems/Application of Artificial Intelligence 7. Predictive Models in Precision Medicine 8. Deep Neural Networks for Phenotype Prediction: Application to rare diseases 9. Artificial Intelligence in the management of patients with intracranial neoplasms 10. Artificial Intelligence to aid the early detection of Mental Illness 11. Use of Artificial Intelligence in Alzheimer Disease Detection 12. Artificial Intelligence to predict atheroma plaque vulnerability 13. Decision support systems in cardiovascular medicine through artificial intelligence: applications in the diagnosis of infarction and prognosis of heart failure 14. Artificial Intelligence for Decision Support Systems in Diabetes 15. Clinical decision support systems to improve the diagnosis and management of respiratory diseases 16. Use of Artificial Intelligence in Neurosurgery and Otorhinolaryngology (Head and Neck Surgery) 17. Use of Artificial Intelligence in Emergency Medicine 18. Use of Artificial Intelligence in Infectious diseases 19. Artificial Intelligence techniques applied to patient care and monitoring 20. Use of artificial intelligence in precision nutrition and fitness

Section III: Precision Systems 21. Artificial Intelligence in Precision health: Systems in practice

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Artificial intelligence has given us algorithms capable of recognizing faces, diagnosing disease, and of course, crushing computer games. But even the smartest algorithms can sometimes behave in unexpected and unwanted waysfor example, picking up gender bias from the text or images they are fed.

A new framework for building AI programs suggests a way to prevent aberrant behavior in machine learning by specifying guardrails in the code from the outset. It aims to be particularly useful for nonexperts deploying AI, an increasingly common issue as the technology moves out of research labs and into the real world.

The approach is one of several proposed in recent years for curbing the worst tendencies of AI programs. Such safeguards could prove vital as AI is used in more critical situations, and as people become suspicious of AI systems that perpetuate bias or cause accidents.

Last week Apple was rocked by claims that the algorithm behind its credit card offers much lower credit limits to women than men of the same financial means. It was unable to prove that the algorithm had not inadvertently picked up some form of bias from training data. Just the idea that the Apple Card might be biased was enough to turn customers against it.

Similar backlashes could derail adoption of AI in areas like health care, education, and government. People are looking at how AI systems are being deployed and they're seeing they are not always being fair or safe, says Emma Brunskill, an assistant professor at Stanford and one of the researchers behind the new approach. We're worried right now that people may lose faith in some forms of AI, and therefore the potential benefits of AI might not be realized.

Examples abound of AI systems behaving badly. Last year, Amazon was forced to ditch a hiring algorithm that was found to be gender biased; Google was left red-faced after the autocomplete algorithm for its search bar was found to produce racial and sexual slurs. In September, a canonical image database was shown to generate all sorts of inappropriate labels for images of people.

Machine-learning experts often design their algorithms to guard against certain unintended consequences. But thats not as easy for nonexperts who might use a machine-learning algorithm off the shelf. Its further complicated by the fact that there are many ways to define fairness mathematically or algorithmically.

The new approach proposes building an algorithm so that, when it is deployed, there are boundaries on the results it can produce. We need to make sure that it's easy to use a machine-learning algorithm responsibly, to avoid unsafe or unfair behavior, says Philip Thomas, an assistant professor at the University of Massachusetts Amherst who also worked on the project.

The researchers demonstrate the method on several machine-learning techniques and a couple of hypothetical problems in a paper published in the journal Science Thursday.

First, they show how it could be used in a simple algorithm that predicts college students' GPAs from entrance exam resultsa common practice that can result in gender bias, because women tend to do better in school than their entrance exam scores would suggest. In the new algorithm, a user can limit how much the algorithm may overestimate and underestimate student GPAs for male and female students on average.

In another example, the team developed an algorithm for balancing the performance and safety of an automated insulin pump. Such pumps decide how much insulin to deliver at mealtimes, and machine learning can help determine the right dose for a patient. The algorithm they designed can be told by a doctor to only consider dosages within a particular range, and to have a low probability of suggesting dangerously low or high blood sugar levels.

We need to make sure that it's easy to use a machine-learning algorithm responsibly, to avoid unsafe or unfair behavior.

Philip Thomas, University of Massachusetts

The researchers call their algorithms Seldonian in reference to Hari Seldon, a character in Isaac Asimov stories that feature his famous three laws of robotics, which begin with the rule: A robot may not injure a human being or, through inaction, allow a human being to come to harm.

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Researchers Want Guardrails to Help Prevent Bias in AI - WIRED