Lifeboat Foundation

For a few lucky patients, 2016 was the year when gene therapy turned from promises to cures. The technology, long contemplated as a way to erase disease by revising people’s DNA, made big advances and began turning into a real business offering some of the world’s most expensive and revolutionary medicines.

So what is gene therapy, anyway? The US Food and Drug Administration says it’s any treatment in which a replacement gene is added to a person’s body or a disease-causing one is inactivated. That’s usually done by adding­­­­­­ new instructions to cells via billions of viruses stuffed with correct DNA strands.

It sounds complicated, and it is. Gene therapy was first tested in a person in 1990, but scary side effects turned the gene-fix idea into a scientific backwater. And the field hasn’t conquered all its problems. We started the year with the tale of Glybera, heralded as the first gene treatment ever approved that sought to correct an inherited gene error. Yet the drug came with an eye-popping price tag of $1 million and, dogged by questions over how well it works, has turned into a medical and commercial flop.

Continue reading “Gene therapy is here, and it could radically change modern medicine” »

WHY THIS MATTERS IN BRIEF Every business is becoming a technology business and nowhere is that truer than in the financial services industry, now as banks try to compete with start ups and established technology companies for tech talent they could find themselves getting into warm water…

One of the distinct advantages of working in the IT industry for over 35 years is all of the direct and indirect experience that brings, as well as the hindsight that comes with that.

One of the more personally interesting experiences for me has been watching the growth and ultimate success of the Open Source Software (OSS) movement from a fringe effort (what business would ever run on OSS?) to what has now become a significant component behind the overall success of the Internet. I was initially reminded of the significance of the Open Source Software movement, and how long it’s actually been around when the technology press recognized the 25th anniversary of the Linux kernel. That, and the decision in January of 1998 by Netscape Communications Corp to release the complete source code for the Communicator web browser, are two of the top reasons for the Internet taking off. Well, the first specification for HTTP helped a little as well, I suppose.

There are, of course, many other examples of OSS software that power the Internet, from the numerous Apache Foundation projects, relational and other database management systems like Postgres, MySQL, MongoDB, and Cassandra. The list of markets and technologies for which there are OSS resources is essentially endless.

Not sure that I would claim 2016 as the year that AI exploded; I believe a better term for 2016 is the year that AI reinvented itself. I still see us in an evolution trend in 2017 as we still need to see more AI technology embedded in our back office platforms and apps than where we are today to claim we’re in a real AI explosion. Once we start seeing more IT organizations and CxOs embracing it in lowering their operational costs then we can claim we’re in an explosion.

Artificial intelligence isn’t a new concept. It is something that companies and businesses have been trying to implement (and something that society has feared) for decades. However, with all the recent advancements to democratize artificial intelligence and use it for good, almost every company started to turn to this technology and technique in 2016.

The year started with Facebook’s CEO Mark Zuckerberg announcing his plan to build an artificially intelligent assistant to do everything from adjusting the temperature in his house to checking up on his baby girl. He worked throughout the year to bring his plan to life, with an update in August that stated he was almost ready to show off his AI to the world.

Continue reading “2016: The year artificial intelligence exploded” »

Hmmm.

Technological and human rights implications for the world

China adopted the highly controversial cyber security law on 7th November 2016. The legislation which will take effect in June 2017 was passed by its largely rubber – stamp parliament emphasizing the ‘objective need’ of China as a major internet power. The stated objective of the law is to counter the growing threats such as hacking and terrorism. Overseas critics of the law are not amused as it has already triggered concerns among foreign business and rights groups that the law threatens to shut foreign technology companies out of various sectors which China deems as ‘critical’. The legislation also incorporates contentious requirements for security reviews and for data to be stored on servers in China.

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Summary:

• Clinical trial failure rates for small molecules in oncology exceed 94% for molecules previously tested in animals and the costs to bring a new drug to market exceed $2.5 billion
• There are around 2,000 drugs approved for therapeutic use by the regulators with very few providing complete cures
• Advances in deep learning demonstrated superhuman accuracy in many areas and are expected to transform industries, where large amounts of training data is available
• Generative Adversarial Networks (GANs), a new technology introduced in 2014 represent the “cutting edge” in artificial intelligence, where new images, videos and voice can be produced by the deep neural networks on demand
• Here for the first time we demonstrate the application of Generative Adversarial Autoencoders (AAEs), a new type of GAN, for generation of molecular fingerprints of molecules that kill cancer cells at specific concentrations
• This work is the proof of concept, which opens the door for the cornucopia of meaningful molecular leads created according to the given criteria
• The study was published in Oncotarget and the open-access manuscript is available in the Advance Open Publications section
• Authors speculate that in 2017 the conservative pharmaceutical industry will experience a transformation similar to the automotive industry with deep learned drug discovery pipelines integrated into the many business processes
• The extension of this work will be presented at the “4th Annual R&D Data Intelligence Leaders Forum” in Basel, Switzerland, Jan 24-26th, 2017

Thursday, 22nd of December Baltimore, MD — Scientists at the Pharmaceutical Artificial Intelligence (pharma. AI) group of Insilico Medicine, Inc, today announced the publication of a seminal paper demonstrating the application of generative adversarial autoencoders (AAEs) to generating new molecular fingerprints on demand. The study was published in Oncotarget on 22nd of December, 2016. The study represents the proof of concept for applying Generative Adversarial Networks (GANs) to drug discovery. The authors significantly extended this model to generate new leads according to multiple requested characteristics and plan to launch a comprehensive GAN-based drug discovery engine producing promising therapeutic treatments to significantly accelerate pharmaceutical R&D and improve the success rates in clinical trials.

Definitely; why we been telling businesses the importance of BMI for their environments.

A site dedicated to the sciences, recent scientific discoveries and advances.

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Is there something your small business could do to impart a little magic onto the world? Using your problem solving skills may help you do just that.

Artificial intelligence (AI) technology has the potential to boost productivity but increase wealth inequality and wipe out millions of jobs, a research report by the White House claimed on Tuesday. With an increasing number of industries set to be affected by automation technology in the coming years, jobs could be displaced — a fear that has been voiced by academics and business leaders. Auto companies are developing driverless cars, and factories are seeing an increased use of robotics.

Because AI is not a single technology, but rather a collection of technologies that are applied to specific tasks, the effects of AI will be felt unevenly through the economy. Some tasks will be more easily automated than others, and some jobs will be affected more than others — both negatively and positively.

Researchers around the world have given varying estimates about the size of potential job losses. One recent estimate by Forrester suggests 6 percent of jobs in the next five years could be wiped out thanks to AI. The White House report cites a 2013 study from Oxford University suggesting that 47 percent of U.S. jobs are at risk because of AI. The report suggests that lower-skilled and less-educated workers could feel the heat the most. Overall, the White House report advocates a three-pronged approach to preparing for a future remade by AI that includes investing in AI for its benefits, training Americans for the jobs of the future and helping workers make the transition to new positions.

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