Posts Tagged ‘Open source intelligence’

Future of Big Data for Societal Transformation

Posted in Artificial Intelligence - Breakthroughs in Theories and Technologies, Big Data, Intelligent Information Systems, tagged Artificial intelligence, Open source intelligence on December 14, 2015| Leave a Comment »

Future of Big Data for Societal Transformation

Larry H. Bernstein, MD, FCAP, Curator

LPBI

 

Musk, others commit $1 billion to non-profit AI research company to ‘benefit humanity’

Open-sourcing AI development to prevent an AI superpower takeover

http://www.kurzweilai.net/musk-others-commit-1-billion-to-non-profit-ai-research-company-to-benefit-humanity

(credit: OpenAI)

Elon Musk and associates announced OpenAI, a non-profit AI research company, on Friday (Dec. 11), committing $1 billion toward their goal to “advance digital intelligence in the way that is most likely to benefit humanity as a whole, unconstrained by a need to generate financial return.”

The funding comes from a group of tech leaders including Musk, Reid Hoffman, Peter Thiel, and Amazon Web Services, but the venture expects to only spend “a tiny fraction of this in the next few years.”

The founders note that it’s hard to predict how much AI could “damage society if built or used incorrectly” or how soon. But the hope is to have a leading research institution that can “prioritize a good outcome for all over its own self-interest … as broadly and evenly distributed as possible.”

Brains trust

OpenAI’s co-chairs are Musk, who is also the principal funder of Future of Life Institute, and Sam Altman, president of  venture-capital seed-accelerator firm Y Combinator, who is also providing funding.

“I think the best defense against the misuse of AI is to empower as many people as possible to have AI. If everyone has AI powers, then there’s not any one person or a small set of individuals who can have AI superpower.” — Elon Musk on Medium

The founders say the organization’s patents (if any) “will be shared with the world. We’ll freely collaborate with others across many institutions and expect to work with companies to research and deploy new technologies.”

OpenAI’s research director is machine learning expert Ilya Sutskever, formerly at Google, and its CTO is Greg Brockman, formerly the CTO of Stripe. The group’s other founding members are “world-class research engineers and scientists” Trevor Blackwell, Vicki Cheung, Andrej Karpathy, Durk Kingma, John Schulman, Pamela Vagata, and Wojciech Zaremba. Pieter Abbeel, Yoshua Bengio, Alan Kay, Sergey Levine, and Vishal Sikka are advisors to the group. The company will be based in San Francisco.

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If I’m Dr. Evil and I use it, won’t you be empowering me?

“There are a few different thoughts about this. Just like humans protect against Dr. Evil by the fact that most humans are good, and the collective force of humanity can contain the bad elements, we think its far more likely that many, many AIs will work to stop the occasional bad actors than the idea that there is a single AI a billion times more powerful than anything else. If that one thing goes off the rails or if Dr. Evil gets that one thing and there is nothing to counteract it, then we’re really in a bad place.” — Sam Altman in an interview with Steven Levy on Medium.

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The announcement follows recent announcements by Facebook to open-source the hardware design of its GPU-based “Big Sur” AI server (used for large-scale machine learning software to identify objects in photos and understand natural language, for example); by Google to open-source its TensorFlow machine-learning software; and by Toyota Corporation to invest $1 billion in a five-year private research effort in artificial intelligence and robotics technologies, jointly with Stanford University and MIT.

To follow OpenAI: @open_ai or info@openai.com    Topics: AI/Robotics | Survival/Defense

Spot on Elon! The threat is and currently the developments are unfortunately pointing exactly in that direction that AI will be controlled via a handful of big and powerful cooperation . None surprisingly none of those subjects are part of the OpenAI movement.

I like the sentiment, AI for all and for the common good, and at one level it seems doable but at another level it seems problematic on the scale of nation states and multinational entities.

If we all have AI systems then it will be those with control of the most energy to run their AI who will have the most influence, and that could be a “Dr. Evil”. It is the sum total of computing power on any given side of a conflict that will determine the outcome, if AI is a significant factor at all.

We could see bigger players looking at strategic questions such as, do they act now, or wait and put more resources into advancing the power of their AI so that they have better odds later, but at the risk of falling to a preemptive attack. Given this sort of thing I don’t see that AI will be a game changer, a leveller, rather it could just fit into the existing arms race type scenarios, at least until one group crosses a singularity threshold and then accelerates away from the pack while holding everyone else back so that they cannot catch up.

Not matter how I look at it I always see the scenarios running in the opposite direction to diversity, toward a singular dominant entity that “roots” all the other AI, sensor and actuator systems and then assimilates them.

How do they plan to stop this? How can one group of AIs have an ethical framework that allows them to “keep down” another group or single AI so that it does not get into a position to dominate them? How will this be any less messy than how the human super-powers have interacted in the last century?

 

I recommend the book “SuperIntelligence” by Nick Bostrom. Most thorough and penetrating. It covers many permutations of the intelligence explosion. The Allegory at the beginning is worth the price alone.

 

Elon, for goodness sake, focus! Get the big battery factory working, get space industry off the ground and America back in the ISS resupply and re-crew business, but enough with the non-profit expenditures already! Keep sinking your capital into non profits like the Hyperlink-a beautiful, high tech version of the old “I just know I can make trains profitable again outside of the northeast” dream and this non-profit AI and you’ll eventually go one financial step too far.

Both for you and for all of us who benefit from your efforts, consider this. At least change your attitude about profit; keep the option open that this AI will bring some profit, even with the open source aspect. This is a great effort, as I see you possibly becoming the “good AI” element that Ray writes about in his first essay, in the essay section on this site. There, Ray is confident that the good people with AI will out-think the bad people with AI and so good AI will prevail.

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Artificial Intelligence Versus the Scientist: Who Will Win?

Posted in Academic Publishing, Biological Networks, BioTechnology - Venture Creation, CANCER BIOLOGY & Innovations in Cancer Therapy, Computational Biology/Systems and Bioinformatics, Curation, Curation methodology, De novo synthesis, Discovery process, Disease Biology, Evolutionary cognition, Experimental validation, Explanatory, Gene Regulation and Evolution, Historical relevance, Interviews with Scientific Leaders, Open Access Journals, Small Molecules in Development of Therapeutic Drugs, Technology Advance Assessment of, Theoretic convergence, tagged #science2_0, @science 2_0, Artificial intelligence, Aviva Lev-Ari, biocuration, Cancer - General, Cell Biology, cognition systems, Computational Medical Science, Computer Model, Content Curation, curation communities, Curation methodology, DARPA, knowledge-based conceptualization, Larry H. Bernstein, learning algorithms, Machine Learning, oncogene, Open access, open innovation, Open source intelligence, Ras, Ras oncogene, research, science on March 2, 2015| Leave a Comment »

Artificial Intelligence Versus the Scientist: Who Will Win?

Will DARPA Replace the Human Scientist: Not So Fast, My Friend!

Writer, Curator: Stephen J. Williams, Ph.D.

Last month’s issue of Science article by Jia You “DARPA Sets Out to Automate Research”[1] gave a glimpse of how science could be conducted in the future: without scientists. The article focused on the U.S. Defense Advanced Research Projects Agency (DARPA) program called ‘Big Mechanism”, a $45 million effort to develop computer algorithms which read scientific journal papers with ultimate goal of extracting enough information to design hypotheses and the next set of experiments,

all without human input.

The head of the project, artificial intelligence expert Paul Cohen, says the overall goal is to help scientists cope with the complexity with massive amounts of information. As Paul Cohen stated for the article:

“‘

Just when we need to understand highly connected systems as systems,

our research methods force us to focus on little parts.

                                                                                                                                                                                                               ”

The Big Mechanisms project aims to design computer algorithms to critically read journal articles, much as scientists will, to determine what and how the information contributes to the knowledge base.

As a proof of concept DARPA is attempting to model Ras-mutation driven cancers using previously published literature in three main steps:

1. Natural Language Processing: Machines read literature on cancer pathways and convert information to computational semantics and meaning

One team is focused on extracting details on experimental procedures, using the mining of certain phraseology to determine the paper’s worth (for example using phrases like ‘we suggest’ or ‘suggests a role in’ might be considered weak versus ‘we prove’ or ‘provide evidence’ might be identified by the program as worthwhile articles to curate). Another team led by a computational linguistics expert will design systems to map the meanings of sentences.

2. Integrate each piece of knowledge into a computational model to represent the Ras pathway on oncogenesis.
3. Produce hypotheses and propose experiments based on knowledge base which can be experimentally verified in the laboratory.

The Human no Longer Needed?: Not So Fast, my Friend!

The problems the DARPA research teams are encountering namely:

• Need for data verification
• Text mining and curation strategies
• Incomplete knowledge base (past, current and future)
• Molecular biology not necessarily “requires casual inference” as other fields do

Verification

Notice this verification step (step 3) requires physical lab work as does all other ‘omics strategies and other computational biology projects. As with high-throughput microarray screens, a verification is needed usually in the form of conducting qPCR or interesting genes are validated in a phenotypical (expression) system. In addition, there has been an ongoing issue surrounding the validity and reproducibility of some research studies and data.

See Importance of Funding Replication Studies: NIH on Credibility of Basic Biomedical Studies

Therefore as DARPA attempts to recreate the Ras pathway from published literature and suggest new pathways/interactions, it will be necessary to experimentally validate certain points (protein interactions or modification events, signaling events) in order to validate their computer model.

Text-Mining and Curation Strategies

The Big Mechanism Project is starting very small; this reflects some of the challenges in scale of this project. Researchers were only given six paragraph long passages and a rudimentary model of the Ras pathway in cancer and then asked to automate a text mining strategy to extract as much useful information. Unfortunately this strategy could be fraught with issues frequently occurred in the biocuration community namely:

Manual or automated curation of scientific literature?

Biocurators, the scientists who painstakingly sort through the voluminous scientific journal to extract and then organize relevant data into accessible databases, have debated whether manual, automated, or a combination of both curation methods [2] achieves the highest accuracy for extracting the information needed to enter in a database. Abigail Cabunoc, a lead developer for Ontario Institute for Cancer Research’s WormBase (a database of nematode genetics and biology) and Lead Developer at Mozilla Science Lab, noted, on her blog, on the lively debate on biocuration methodology at the Seventh International Biocuration Conference (#ISB2014) that the massive amounts of information will require a Herculaneum effort regardless of the methodology.

Although I will have a future post on the advantages/disadvantages and tools/methodologies of manual vs. automated curation, there is a great article on researchinformation.info “Extracting More Information from Scientific Literature” and also see “The Methodology of Curation for Scientific Research Findings” and “Power of Analogy: Curation in Music, Music Critique as a Curation and Curation of Medical Research Findings – A Comparison” for manual curation methodologies and A MOD(ern) perspective on literature curation for a nice workflow paper on the International Society for Biocuration site.

The Big Mechanism team decided on a full automated approach to text-mine their limited literature set for relevant information however was able to extract only 40% of relevant information from these six paragraphs to the given model. Although the investigators were happy with this percentage most biocurators, whether using a manual or automated method to extract information, would consider 40% a low success rate. Biocurators, regardless of method, have reported ability to extract 70-90% of relevant information from the whole literature (for example for Comparative Toxicogenomics Database)[3-5].

Incomplete Knowledge Base

In an earlier posting (actually was a press release for our first e-book) I had discussed the problem with the “data deluge” we are experiencing in scientific literature as well as the plethora of ‘omics experimental data which needs to be curated.

Tackling the problem of scientific and medical information overload

Figure. The number of papers listed in PubMed (disregarding reviews) during ten year periods have steadily increased from 1970.

Analyzing and sharing the vast amounts of scientific knowledge has never been so crucial to innovation in the medical field. The publication rate has steadily increased from the 70’s, with a 50% increase in the number of original research articles published from the 1990’s to the previous decade. This massive amount of biomedical and scientific information has presented the unique problem of an information overload, and the critical need for methodology and expertise to organize, curate, and disseminate this diverse information for scientists and clinicians. Dr. Larry Bernstein, President of Triplex Consulting and previously chief of pathology at New York’s Methodist Hospital, concurs that “the academic pressures to publish, and the breakdown of knowledge into “silos”, has contributed to this knowledge explosion and although the literature is now online and edited, much of this information is out of reach to the very brightest clinicians.”

Traditionally, organization of biomedical information has been the realm of the literature review, but most reviews are performed years after discoveries are made and, given the rapid pace of new discoveries, this is appearing to be an outdated model. In addition, most medical searches are dependent on keywords, hence adding more complexity to the investigator in finding the material they require. Third, medical researchers and professionals are recognizing the need to converse with each other, in real-time, on the impact new discoveries may have on their research and clinical practice.

These issues require a people-based strategy, having expertise in a diverse and cross-integrative number of medical topics to provide the in-depth understanding of the current research and challenges in each field as well as providing a more conceptual-based search platform. To address this need, human intermediaries, known as scientific curators, are needed to narrow down the information and provide critical context and analysis of medical and scientific information in an interactive manner powered by web 2.0 with curators referred to as the “researcher 2.0”. This curation offers better organization and visibility to the critical information useful for the next innovations in academic, clinical, and industrial research by providing these hybrid networks.

Yaneer Bar-Yam of the New England Complex Systems Institute was not confident that using details from past knowledge could produce adequate roadmaps for future experimentation and noted for the article, “ “The expectation that the accumulation of details will tell us what we want to know is not well justified.”

In a recent post I had curated findings from four lung cancer omics studies and presented some graphic on bioinformatic analysis of the novel genetic mutations resulting from these studies (see link below)

Multiple Lung Cancer Genomic Projects Suggest New Targets, Research Directions for

Non-Small Cell Lung Cancer

which showed, that while multiple genetic mutations and related pathway ontologies were well documented in the lung cancer literature there existed many significant genetic mutations and pathways identified in the genomic studies but little literature attributed to these lung cancer-relevant mutations.

  This ‘literomics’ analysis reveals a large gap between our knowledge base and the data resulting from large translational ‘omic’ studies.

A ‘literomics’ approach focuses on what we don NOT know about genes, proteins, and their associated pathways while a text-mining machine learning algorithm focuses on building a knowledge base to determine the next line of research or what needs to be measured. Using each approach can give us different perspectives on ‘omics data.

Deriving Casual Inference

Ras is one of the best studied and characterized oncogenes and the mechanisms behind Ras-driven oncogenenis is highly understood.   This, according to computational biologist Larry Hunt of Smart Information Flow Technologies makes Ras a great starting point for the Big Mechanism project. As he states,” Molecular biology is a good place to try (developing a machine learning algorithm) because it’s an area in which common sense plays a minor role”.

Even though some may think the project wouldn’t be able to tackle on other mechanisms which involve epigenetic factors UCLA’s expert in causality Judea Pearl, Ph.D. (head of UCLA Cognitive Systems Lab) feels it is possible for machine learning to bridge this gap. As summarized from his lecture at Microsoft:

“The development of graphical models and the logic of counterfactuals have had a marked effect on the way scientists treat problems involving cause-effect relationships. Practical problems requiring causal information, which long were regarded as either metaphysical or unmanageable can now be solved using elementary mathematics. Moreover, problems that were thought to be purely statistical, are beginning to benefit from analyzing their causal roots.”

According to him first

1) articulate assumptions

2) define research question in counter-inference terms

Then it is possible to design an inference system using calculus that tells the investigator what they need to measure.

To watch a video of Dr. Judea Pearl’s April 2013 lecture at Microsoft Research Machine Learning Summit 2013 (“The Mathematics of Causal Inference: with Reflections on Machine Learning”), click here.

The key for the Big Mechansism Project may me be in correcting for the variables among studies, in essence building a models system which may not rely on fully controlled conditions. Dr. Peter Spirtes from Carnegie Mellon University in Pittsburgh, PA is developing a project called the TETRAD project with two goals: 1) to specify and prove under what conditions it is possible to reliably infer causal relationships from background knowledge and statistical data not obtained under fully controlled conditions 2) develop, analyze, implement, test and apply practical, provably correct computer programs for inferring causal structure under conditions where this is possible.

In summary such projects and algorithms will provide investigators the what, and possibly the how should be measured.

So for now it seems we are still needed.

References

1. You J: Artificial intelligence. DARPA sets out to automate research. Science 2015, 347(6221):465.
2. Biocuration 2014: Battle of the New Curation Methods [http://blog.abigailcabunoc.com/biocuration-2014-battle-of-the-new-curation-methods]
3. Davis AP, Johnson RJ, Lennon-Hopkins K, Sciaky D, Rosenstein MC, Wiegers TC, Mattingly CJ: Targeted journal curation as a method to improve data currency at the Comparative Toxicogenomics Database. Database : the journal of biological databases and curation 2012, 2012:bas051.
4. Wu CH, Arighi CN, Cohen KB, Hirschman L, Krallinger M, Lu Z, Mattingly C, Valencia A, Wiegers TC, John Wilbur W: BioCreative-2012 virtual issue. Database : the journal of biological databases and curation 2012, 2012:bas049.
5. Wiegers TC, Davis AP, Mattingly CJ: Collaborative biocuration–text-mining development task for document prioritization for curation. Database : the journal of biological databases and curation 2012, 2012:bas037.

Other posts on this site on include: Artificial Intelligence, Curation Methodology, Philosophy of Science

Inevitability of Curation: Scientific Publishing moves to embrace Open Data, Libraries and Researchers are trying to keep up

A Brief Curation of Proteomics, Metabolomics, and Metabolism

The Methodology of Curation for Scientific Research Findings

Scientific Curation Fostering Expert Networks and Open Innovation: Lessons from Clive Thompson and others

The growing importance of content curation

Data Curation is for Big Data what Data Integration is for Small Data

Cardiovascular Original Research: Cases in Methodology Design for Content Co-Curation The Art of Scientific & Medical Curation

Exploring the Impact of Content Curation on Business Goals in 2013

Power of Analogy: Curation in Music, Music Critique as a Curation and Curation of Medical Research Findings – A Comparison

conceived: NEW Definition for Co-Curation in Medical Research

Reconstructed Science Communication for Open Access Online Scientific Curation

Search Results for ‘artificial intelligence’

 The Simple Pictures Artificial Intelligence Still Can’t Recognize

Data Scientist on a Quest to Turn Computers Into Doctors

Vinod Khosla: “20% doctor included”: speculations & musings of a technology optimist or “Technology will replace 80% of what doctors do”

Where has reason gone?

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Cardiac Ca2+ Signaling: Transcriptional Control

Posted in Biological Networks, Cardiovascular Pharmacogenomics, Cell Biology, Disease Biology, Gene Regulation and Evolution, Genetics & Pharmaceutical, Genome Biology, Molecular Genetics & Pharmaceutical, Nutrition and Phytochemistry, Pharmaceutical Analytics, Pharmaceutical Industry Competitive Intelligence, Population Health Management, Signaling & Cell Circuits, Small Molecules in Development of Therapeutic Drugs, tagged Ca2+/calmodulin-dependent protein kinase, Calcium in biology, Green fluorescent protein, health, heart, Open source intelligence, Protocols, Second messenger system, Signal transduction, Transcription (genetics) on March 2, 2013| 2 Comments »

Cardiac Ca2+ Signaling: Transcriptional Control

Reporter: Larry H Bernstein, MD, FCAP

The other side of cardiac Ca2+ signaling: transcriptional control

A Domínguez-Rodríguez, G Ruiz-Hurtado, Jean-Pierre Benitah and AM Gómez

• Ca2+ is not only a key element in excitation-contraction coupling (EC coupling), but
• it is also a pivotal second messenger in cardiac signal transduction,
• being able to control processes such as
  • excitability,
  • metabolism, and
  • transcriptional regulation.

Front. Physio. 2012; 3:452.                 http://dx.doi.org/fphys.2012.00452/
http://www.fphys.com/The other side of cardiac Ca2+ signaling: transcriptional control

calcium release calmodulin

English: A rendition of the CaMKII holoenzyme in the (A) Closed and the (B) Open conformation (Photo credit: Wikipedia)

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• Ca2+/Calmodulin-Dependent Protein Kinase II (CaMKII) Activity and Sinoatrial Nodal Pacemaker Cell Energetics (plosone.org)
• The L-type Ca2+ Channels Blocker Nifedipine Represses Mesodermal Fate Determination in Murine Embryonic Stem Cells (plosone.org)
• Calreticulin Induces Dilated Cardiomyopathy (plosone.org)
• Phosphorylation and Feedback Regulation of Metabotropic Glutamate Receptor 1 by Calcium/Calmodulin-Dependent Protein Kinase II (jneurosci.org)
• Watching Neurons in action (greenfluorescentblog.wordpress.com)

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