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Google has been increasing their presence in the healthcare space over the past few years. It's a common trend seen amongst many tech companies in the Silicon Valley. I was surprised to note that even companies like Samsung, Fujifilm, Sony - all have biotech divisions as part of a large conglomerate.1. Google recently purchased Lift Labs, a company which has developed a "smart spoon" that accounts and counteracts the tremor experienced by people suffering from neurodegenerative diseases like Parkinson's disease.Lift Labs Website - A device to stabilize tremorImage Source: Lift Labs2. Brian Otis, from the Google X Life-Sciences team is spearheading the project on contact lenses which can monitor glucose levels through tears. The project started at University of Washington, and is now an integral part of the GoogleX LifeSciences division. Google has also collaborated with Alcon, the ophthalmology division of Novartis, to develop and license the "Smart Lens" technology.Source: Alcon announcement 3. Andrew Conrad recently mentioned that GoogleX is developing a nanoparticle pill which can identify cancers and heart-attacks. The firm is trying to work on pre-emptive or proactive medication which might involve constant monitoring of health, and detection of tiny changes which could prevent conditions prior to the development of complications. Andrew Conrad, in collaboration with Stanford and Duke, are involved in a study of 175 healthy human volunteers. The clinical data generated will be used as a "Baseline Study". The data will provide further insight in knowing how the human body behaves when it's healthy.Source: Google X sets out to define healthy human4. Google has partnerships with (or invested in) several small companies, namely:- Rani Therapeutics (Oral Delivery of large molecules like rh-insulin and adalimumab)- DNANexus (Bioinformatics, genomics and cloud computing)- SynapDx (Diagnostics for early detection of Autism Spectrum Disorder)- Transcriptic (Genotyping, Molecular Cloning, Biobanking)__________________________________________________________(01.28.2015) Update: Google, Biogen Seek Reasons for Advance of Multiple Sclerosis"Using sensors, software and data analysis tools, the companies will collect and sift through data from people with the disease. The goal is to explain why multiple sclerosis progresses differently from patient to patient, said Rick Rudick, Biogen’s vice president of development sciences."(03.01.2015) Update: Why Is Google Making Human Skin?In addition to collaborating with Institute for Systems Biology (ISB) to create the Cancer Genomics Cloud, Life Sciences Division at Google is working on cancer detection achieved by the attachment of nanoparticles and cancerous cells. Upon attachment, the cancerous cells are expected to "light up", allowing for easy detection by a photometric device.(12.09.2015) Update: Google Life Sciences debuts a new name, VerilyGoogle Life Sciences of Google X projects rebrands itself as Verily, as a subsidiary of Alphabet Inc.(12.17.2015) Update: Google and Johnson & Johnson Conjugate to Create Verb Surgical, Promise Fancy Medical RobotsVerily collaborates with Ethicon, a J&J medical device subsidiary to announce the formation of a new surgical start-up called Verb. Verb aims to develop comprehensive surgical solutions that will integrate with advanced technologies such imaging, data analysis, machine learning etc.

The best healthcare is found at academic teaching research hospitals. By and large these are top USA medical teaching hospitals, Harvard, Anderson Texas, UCSF, UCLA, Johns Hopkins, Sloan Memorial Kettering, Mayo Clinic, Stanford, Yale, etc ...For 2016 the ranking are US News Hospital Rankings by specialty and overall. It is no accident that these are all by and large all teaching medical research hospitals.In the commercial healthcare delivery system the standard of care is always substandard, intentionally trading dollars for lives. New life saving treatments are only implemented after years of clinic trails and only when the treatment becomes cheap. That is the overriding issue in the for profit health care. It is not about what is possible but what can be paid for while making a profit.Academic teaching research hospitals have the following advantagesthey are typically non profits focusing on the best standard of care with cost as a secondary issueteaching doctors in these hospitals are on average the most proficient and advanced in the healthcare system as they are tasked with training resident doctors with the latest and greatest medical techniques. Therefore their skillset is always current and moving forward to the highest possible levelteaching doctors are tasked with improving the standard of care, moving the field forward, doctor in commercial settings are simply paid to practice what the dated standard of care.academic teaching research hospitals garner the vast majority of medical research dollars as these institutions alone have the talented doctors that can conduct research in an effective efficient mannerteaching doctors are often the best of best in the realm of doctors, one's care is directly determined by the proficeiny of one's doctorMost importantly these doctors are typically driven to make a difference, to leave a lasting mark and passionate about improvement.These doctors see the most challenging difficult cases which is turn further improves their skillset going forward.

Hard to believe that it’s only been a year... So much has happened in the world of AI and machine learning that it is hard to fit in a single answer. Here is my attempt. Don’t expect too many details, but do expect a lot of links to follow up on them.If I have to pick my main highlight of the year, that has to go to AlphaGo Zero (paper). Not only does this new approach improve in some of the most promising directions (e.g. deep reinforcement learning), but it also represents a paradigm shift in which such models can learn without data. We have also learned very recently that the Alpha Go Zero generalizes to other games such as chess. You can read more about my interpretation of this advance in my Quora answer.A recent meta-study found systematic mistakes in reporting metrics on GANs-related research papers. Despite this, it is undeniable that GANs have continued to present impressive results, especially when it comes to their applications to the image space (e.g. Progressive GANs, Conditional GANS in pix2pix, or CycleGans) . NLP is another area that has seen very impressive advances due to Deep Learning this year is NLP, and, in particular, translation. Salesforce presented an interesting non-autoregressive approach that can tackle full sentence translation. Perhaps even more groundbreaking are the unsupervised approaches presented by Facebook and UPV. Deep Learning is also having a huge impact in an area that hits close to home: recommender systems. However, a recent paper also questioned some recent advances by showing how much simpler methods like kNN were competitive with Deep Learning. It is also not a surprise that, as in the case of GAN research, the incredible fast pace of AI research can also lead to some loss in scientific rigor. Let me also point out that while it is true that many or most AI advances are coming from the Deep Learning field, there is continuous innovation in many other directions in AI and ML.Somewhat related to some of the issues mentioned above, many criticize this lack of rigor and investment in setting the theoretical foundations of the methods. Just this week, Ali Rahimi described modern AI as “alchemy” in his NIPS 2017 Test of Time talk. This was quickly responded by Yann Lecun in a debate that is unlikely to be resolved any time soon. I think you might agree though that this year has seen many interesting efforts in trying to advance the foundations of Deep Learning. For example, researchers are trying to understand how deep neural networks generalize. Tishby’s Information Bottleneck theory was also debated at length this year as a plausible explanation to some of the Deep Learning properties. Hinton, who is being celebrated for his career this year, also keeps questioning foundational issues such as the use of backpropagation. Renowned researchers such as Pedro Domingos soon picked up the glove and developed Deep Learning methods that used different optimization techniques. A final, and very recent, fundamental change proposed by Hinton is the use of capsules ( see original paper) as an alternative to Convolutional Networks.If we look at the engineering side of AI, the year started with Pytorch picking up steam and becoming a real challenge to Tensorflow, especially in research. Tensorflow quickly reacted by releasing dynamic networks in Tensorflow Fold. The “AI War” between big players has many other battles though, with the most heated one happening around the Cloud. All the main providers have really stepped up and increase their AI support in the cloud. Amazon has presented large innovations in their AWS, such as their recent presentation of Sagemaker to build and deploy ML models, or their Gluon library, released together with Microsoft . It is also worth mentioning that smaller players are also jumping in. Nvidia, has recently introduced their GPU cloud, which promises to be another interesting alternative to train Deep Learning models. Despite all these battles, it is good to see that industry can come together when necessary. The new ONNX standardization of neural network representations is an important and necessary step forward to interoperability.2017 has also seen the continuation (escalation?) of social issues around AI. Elon Musk continues to fuel the idea that we are getting closer and closer to killer AIs, to many people’s dismay. There has also been a lot of discussion about how AI will affect jobs in the next few years. Finally, we have seen a lot more focus being put on transparency and bias of AI algorithms.Finally for the past few months I have been working on AI for medicine and healthcare. I am also happy to see that the rate of innovation in less “traditional” domains like healthcare is quickly picking up. AI and ML have been applied to medicine with years, starting with expert and Bayesian systems in the 60s and 70s. However, I often find myself citing papers that are only a few months old. Some of the recent innovations presented this year include the use of Deep RL, GANs, or Autoencoders to represent patient phenotypes. A lot of recent AI advances have also focused on Precision Medicine (highly personalized medical diagnosis and treatment) and genomics. For example David Blei’s latest paper addresses causality in neural network models by using bayesian inference to predict whether an individual has a genetic predisposition to a disease. All the big players are investing in AI in Healthcare. Google has several teams, including Deepmind Healthcare, who have presented several very interesting advances in AI for medicine, especially in automating medical imaging or the work that Fei Fei’s group is doing between Google and Stanford. But, also Apple is finding healthcare applications for their Apple Watch, and Amazon is “secretly” investing in healthcare. It is clear the space is ripe for innovation.Update 12/28:Thanks for all the comments and upvotes!They are part of the reason why, just a couple of weeks after I wrote the answer, and before the end of 2017, I feel compelled to update the answer and add a couple more recent developments that are worth adding:First, the Uber AI team presented very interesting work on using Genetic Algorithms (GA) in the context of Deep Reinforcement Learning. In a collection of 5 papers, the team shows how GA are a competitive alternative to SGD to train Deep models in situations where they had not been expected to perform well. It is extremely interesting to see GA making a comeback and I am excited to see where this can take us in the next few months.Finally, I recently read a Science paper on how Libratus had beat expert humans on heads-up no-limit Poker (this is a version of an earlier IJCAI paper). This reminded me that I had not mentioned all the exciting advances on imperfect information games that have happened in the last 12 months. While, AlphaGo Zero is indeed a very exciting development, the truth is that most problems in reality can be more easily assimilated to imperfect information games like Poker than to perfect information games like Go or Chess. That is why work in this area is really exciting an important to push the field forward. Besides the recent publication mentioned above, I would probably add the two following: Deep Reinforcement Learning from Self-Play in Imperfect-Information Games, and DeepStack: Expert-Level Artificial Intelligence in Heads-Up No-Limit Poker.