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2017 begins on Monday in Vancouver, Canada, and will explore the theme “The Future You.” If the future you is anything like the future us, you are likely curled up in a big cushy chair right now, devouring the contents of a book that flips your thinking. Below, some reading suggestions from the speaker program. Read, enjoy and stay tuned to the TED Blog for beat-by-beat coverage of the conference.

TED2017 begins on Monday in Vancouver, Canada, and will explore the theme “The Future You.” If the future you is anything like the future us, you are likely curled up in a big cushy chair right now, devouring the contents of a book that flips your thinking. Below, some reading suggestions from the speaker program. Read, enjoy and stay tuned to the TED Blog for beat-by-beat coverage of the conference.

Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy by Cathy O’Neil. The decisions that affect our lives are no longer made by humans — they’re made by algorithms. This might sound like a great way around bias and discrimination, but these things are often built right into our mathematical models. When it comes to college admissions, decisions on parole, applications to jobs and the affects of a bad credit score, O’Neil explores the unintended consequences of algorithms. (Read an excerpt.)

The Telomere Effect: A Revolutionary Approach to Living Younger, Healthier, Longer by Elizabeth Blackburn and Elissa Epel. Molecular biologist Elizabeth Blackburn received the Nobel Prize in Medicine for her discovery of telomeres, the ends of chromosomes that — like shoelace tips — keep our genetic information from fraying. Both telomeres and telomerase, an enzyme that restores worn-down telomeres, appear central to the aging process. This book looks at the research — then turns its attention to how our thoughts, bodies and social worlds affect us on the cellular level.

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Artificial intelligence algorithms are being taught to generate art, human voices, and even fiction stories all on their own—why not give them a shot at building new ways to treat disease?

Atomwise, a San Francisco-based startup and Y Combinator alum, has built a system it calls AtomNet (pdf), which attempts to generate potential drugs for diseases like Ebola and multiple sclerosis. The company has invited academic and non-profit researchers from around the country to detail which diseases they’re trying to generate treatments for, so AtomNet can take a shot. The academic labs will receive 72 different drugs that the neural network has found to have the highest probability of interacting with the disease, based on the molecular data it’s seen.

Atomwise’s system only generates potential drugs—the compounds created by the neural network aren’t guaranteed to be safe, and need to go through the same drug trials and safety checks as anything else on the market. The company believes that the speed at which it can generate trial-ready drugs based on previous safe molecular interactions is what sets it apart.

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Artificial intelligence picks up racial and gender biases when learning language from text, researchers say. Without any supervision, a machine learning algorithm learns to associate female names more with family words than career words, and black names as being more unpleasant than white names.

For a study published today in Science, researchers tested the bias of a common AI model, and then matched the results against a well-known psychological test that measures bias in humans. The team replicated in the algorithm all the psychological biases they tested, according to study co-author Aylin Caliskan, a post-doc at Princeton University. Because machine learning algorithms are so common, influencing everything from translation to scanning names on resumes, this research shows that the biases are pervasive, too.

“Language is a bridge to ideas, and a lot of algorithms are built on language in the real world,” says Megan Garcia, the director of New America’s California branch who has written about this so-called algorithmic bias. “So unless an alg is making a decision based only on numbers, this finding is going to be important.”

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Machine learning algorithms and artificial intelligence systems influence many aspects of people’s lives: news articles, movies to watch, people to spend time with, access to credit, and even the investment of capital. Algorithms have been empowered to make such decisions and take actions for the sake of efficiency and speed. Despite these gains, there are concerns about the rapid automation of jobs (even such jobs as journalism and radiology). A better understanding of attitudes toward and interactions with algorithms is essential precisely because of the aura of objectivity and infallibility cultures tend to ascribe to them. This report illustrates some of the shortcomings of algorithmic decisionmaking, identifies key themes around the problem of algorithmic errors and bias, and examines some approaches for combating these problems. This report highlights the added risks and complexities inherent in the use of algorithmic decisionmaking in public policy. The report ends with a survey of approaches for combating these problems.

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“These re-engineered organisms will change our lives over the coming years, leading to cheaper drugs, ‘green’ means to fuel our cars and targeted therapies for attacking ‘superbugs’ and diseases, such as cancer,” wrote Drs. Ahmad Khalil and James Collins at Boston University, who were not involved in the study.

Our brains are often compared to computers, but in truth, the billions of cells in our bodies may be a better analogy. The squishy sacks of goop may seem a far cry from rigid chips and bundled wires, but cells are experts at taking inputs, running them through a complicated series of logic gates and producing the desired programmed output.

Take beta cells in the pancreas, which manufacture and store insulin. If they detect a large spike in blood sugar, then they release insulin; else they don’t. Each cell adheres to commands like these, allowing us—the organism—to operate normally.

This circuit-like nature of cellular operations is not just a handy metaphor. About 50 years ago, scientists began wondering: what if we could hijack the machinery behind these algorithms and reprogram the cells to do whatever we want?

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Billionaire investor Jim Mellon has joined the push to solve age-related diseases and bring rejuvenation biotechnology to the world.

Billionaire biotechnology investor Jim Mellon has unveiled an investment in an ambitious new venture which seeks to tackle ageing and age-related diseases.

Insilico Medicine is a big data analytics company which says its mission is to ‘extend healthy longevity’.

This is a ‘moonshot’ target in health which has seen investment from a number of ambitious research groups in the last few years.

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By Leah Crane

Break out the censor’s black bars for naked singularities. Quantum effects could be obscuring these impossible predictions of general relativity, new calculations show.

Albert Einstein’s classical equations of general relativity do a fairly good job of describing gravity and space-time. But when it comes to the most extreme objects, such as black holes, general relativity runs into problems.

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Interesting link within concerning an injectable interface.

To be able to design a device that measures brain activity an understanding of the brains function is required. This section gives a high-level overview of some of the key elements of brain function. Human brains contain approximately 80 billion neurons, these neurons are interconnected with 7,000 synaptic connections each (on average). The combination of neurons firing and their communication is, in very simple terms the basis of all thoughts conscious and subconscious. Logically if the activity of these neurons and their connections were read in real-time, a sufficiently intelligent algorithm could understand all thoughts present. Similarly, if an input could be given at this level of granularity new thoughts could be implanted.

All human brains abide by the general structure shown in the picture below, certain areas, by and large do certain things. If higher levels of thoughts like creativity, idea generation and concentration want to be read, the frontal lobe is the place to look. If emotions and short-term memory are the target, the temporal lobe is the place to read from.

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Interest in rejuvenation biotechnology is growing rapidly and attracting investors.

- Jim Mellon has made an investment in Insilico Medicine to enable the company to validate the many molecules discovered using deep learning and launch multi-modal biomarkers of human aging

Monday, April 10, 2017, Baltimore, MD — Insilico Medicine, Inc, a big data analytics company applying deep learning techniques to drug discovery, biomarker development, and aging research today announced that it has closed an investment from the billionaire biotechnology investor Jim Mellon. Proceeds will be used to perform pre-clinical validation of multiple lead molecules developed using Insilico Medicine’s drug discovery pipelines and to advance research in deep learned biomarkers of aging and disease.

“Unlike many wealthy business people who rely entirely on their advisors to support their investment in biotechnology, Jim Mellon has spent a substantial amount of time familiarizing himself with recent developments in biogerontology. He does not just come in with the funding, but brings in expert knowledge and a network of biotechnology and pharmaceutical executives, who work very quickly and focus on the commercialization potential. We are thrilled to have Mr. Mellon as one of our investors and business partners”, said Alex Zhavoronkov, PhD, founder, and CEO of Insilico Medicine, Inc.

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