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Fight Against Cancer with Artificial Intelligence and Big Data - OpenMind


This company has developed a new anti-cancer drug (against pancreatic, breast, liver or brain cancer) called BPM 31510, which has been discovered by an algorithm. The major technology companies are using millions of people data to find treatments. In addition to the start-ups, all major technology companies have already begun to apply Big Data and artificial intelligence to the service of health. Big Data and artificial intelligence, combined with genetic analysis, allow researchers to search for and find patterns among patients with rare diseases, who may be separated by distance but carry the same mutation.



It's good to know the context: What is the difference between Data Analytics, Data Analysis, Data Mining, Data Science, Machine Learning, and Big Data? Anaconda is popular in Data Science and Machine Learning communities. First, download this podcast episode where Knowledge Project interviews Prof. Domingos, who wrote the paper we read earlier. For now, the best StackExchange site is There are also many relevant discussions on Quora, for example: What is the difference between Data Analytics, Data Analysis, Data Mining, Data Science, Machine Learning, and Big Data?

MIT model reveals role of inhibitory neurons in the brain


Recent digital tech advancements have produced prototype artificial neurons and light-based neural networks, but we're still discovering ways our brain actually works. Researchers at MIT have built a computational model that could illustrate how inhibitory neurons work efficiently to block others from firing. The team's model, as described in their paper, uses theoretical computer science applied to a "winner-take-all" operation. NEC Professor of Software Science and Engineering at MIT Nancy Lynch led the team, which will present their results at this week's Innovations in Theoretical Computer Science conference.

What AI can tell us about British history - and what it can't


The team of academics, led by professor Nello Cristianini, collaborated closely with the company findmypast, which is digitising historical newspapers from the British Library as part of their British Newspaper Archive project. Over 35 million articles and 28.6 billion words - around 14 per cent of local newspapers from 1800-1950 - were used for the study, which aimed to establish whether major historical and cultural changes could be detected from statistical footprints in the content of the local papers. Professor Nello Cristianini, professor of AI from the department of engineering and mathematics at Bristol, said the study aimed to: "demonstrate an approach to understanding continuity and change in history, based on the distant reading of a vast body of news, which complements what is traditionally done by historians." Tom Lansdall-Welfare, research associate in machine learning in the department of computer science, who led the computational part of the study, said: "We have demonstrated that computational approaches can establish meaningful relationships between a given signal in large-scale textual corpora and verifiable historical moments."

Upping the Ante: Top Poker Pros Face Off vs. Artificial Intelligence-CMU News - Carnegie Mellon University


Artificial Intelligence: Upping the Ante," beginning Jan. 11 at Rivers Casino, poker pros will play a collective 120,000 hands of Heads-Up No-Limit Texas Hold'em over 20 days against a CMU computer program called Libratus. "Since the earliest days of AI research, beating top human players has been a powerful measure of progress in the field," said Tuomas Sandholm, professor of computer science. "We were thrilled to host the first Brains Vs. AI competition with Carnegie Mellon's School of Computer Science at Rivers Casino, and we are looking forward to the rematch," said Craig Clark, general manager of Rivers Casino. "Since the earliest days of AI research, beating top human players has been a powerful measure of progress in the field," said CMU Computer Science Professor Tuomas Sandholm.

10 Offbeat Predictions for Machine Learning in 2017 - DZone Big Data


Even if it may be hidden behind polished marketing speak pushed by major vendors and research firms (e.g., "Cognitive Computing", "Machine Intelligence", or even doomsday-like "Smart Machines"), the Machine Learning genie is out of the bottle without a doubt as its wide-ranging potential across the enterprise has already made it part of the business lexicon. They will keep investing in algorithm-based startups with marketable academic resumes while perpetuating myths and creating further confusion e.g., portraying Machine Learning as synonymous with Deep Learning, completely misrepresenting the differences between Machine Learning algorithms and Machine-learned models or model training and predicting from trained models1. On a slightly more positive note, a small subset of the VC community seems to be waking up to the huge platform opportunity Machine Learning presents. Legacy company executives that opt for getting expensive help from consulting companies in forming their top-down analytics strategy and/or making complex "Big Data" technology components work together before doing their homework on low hanging predictive use cases will find that actionable insights and game-changing ROI will be hard to show.

Learning About Machine Learning APIs With My Algorithmic Rotoscope Work - DZone Big Data


After weeks of playing around, I have a good grasp of what it takes to separate videos into individual images, applying the Algorithmia machine learning filters, and reassembling them as videos. I also have several of my own texture filters created now using the AWS AMI and process provided Algorithmia -- you can learn more about algorithmic rotoscope, and details of what I did via the GitHub project updates. Well, first of all, it uses the Algorithmia API, but I also developed the separation of the videos, applying filters to images, and reassembling the videos as an API. Next, I am going to write-up Algorithmia's business model, using my algorithmic rotoscope work as a hypothetical API-driven business -- helping me think through the economics of building a SaaS or retail API solution on top of Algorithmia.

How Big Data can help make better life-critical decisions


Let's take the work Google DeepMind is undertaking with University College London Hospital NHS Foundation Trust. By applying its DeepMind artificial intelligence to the CT and MRI scans of 700 former cancer patients, Google's technology will quickly distinguish healthy from cancerous tissue. This is where the addition of Deep Learning and Machine Learning (intrinsic parts of Data Science) comes into play. Removing all personal details, for example, means anonymised patient records along with past and present data treatment assessments enable us to analyse and improve our understanding of future treatments.

Model sheds light on purpose of inhibitory neurons

MIT News

In recent years, artificial neural networks -- computer models roughly based on the structure of the brain -- have been responsible for some of the most rapid improvement in artificial-intelligence systems, from speech transcription to face recognition software. Lynch, Parter, and Musco's circuit thus includes feedback: Signals from the output neurons pass to the inhibitory neurons, whose output in turn passes back to the output neurons. In a typical artificial neural net, if a node's input values exceed some threshold, the node fires. The convergence neuron drives the circuit to select a single output neuron, at which point it stops firing; the stability neuron prevents a second output neuron from becoming active once the convergence neuron has been turned off.

IBM Watson: The Growth Story Finally Unfolding


Before Siemens's current initiative of utilizing Watson in the industrial sector, the German conglomerate created a global strategic alliance with IBM to exploit the strength of Watson in healthcare. The ultimate aim of the pact between IBM and Siemens is to deliver automation in the industrial sector by allowing developers to build intelligent apps utilizing Watson's AI capabilities on Siemens's industrial cloud. However, lack of data-driven intelligent apps in the market results in wastage of data since benefits of IBM's cloud platform and analytics solutions can't be fully realized. Per my estimate, IBM's quarterly revenue from Watson (including revenues Watson indirectly drives) currently stands at somewhere below the $1 billion mark, which is about to cross $1 billion in the near-term due to the Siemens deal.