IBM

On Cognitive Computing vs Artificial Intelligence

From Ginni Rometty on Artificial Intelligence – Bloomberg

Doctors don’t want black-and-white answers, nor does any profession. If you’re a professional, my guess is when you interact with AI, you don’t want it to say, “Here is an answer.” What a doctor wants is, “OK, give me the possible answers. Tell my why you believe it. Can I see the research, the evidence, the ‘percent confident’? What more would you like to know?”

and

When I went to Davos in January, we published something called Transparency and Trust in the Cognitive Era. It’s our responsibility if we build this stuff to guide it safely into the world. First, be clear on the purpose, work with man. We aren’t out here to destroy man. The second is to be transparent about who trained the computers, who are the experts, where did the data come from. And when consumers are using AI, you inform them that they are and inform the company as well that owns the intellectual property. And the third thing is to be committed to skill.

IBM and its term “cognitive computing” are all about so-called “weak AI”. The problem is that giving the insight about an answer is incredibly challenging at the moment vs just giving the answer in a black-box fashion.

AI and machine learning algorithms helped predict instances of schizophrenia with 74% accuracy

From IBM News room – 2017-07-21 IBM and University of Alberta Publish New Data on Machine Learning Algorithms to Help Predict Schizophrenia

In the paper, researchers analyzed de-identified brain functional Magnetic Resonance Imaging (fMRI) data from the open data set, Function Biomedical Informatics Research Network (fBIRN) for patients with schizophrenia and schizoaffective disorders, as well as a healthy control group. fMRI measures brain activity through blood flow changes in particular areas of the brain.

Specifically, the fBIRN data set reflects research done on brain networks at different levels of resolution, from data gathered while study participants conducted a common auditory test. Examining scans from 95 participants, researchers used machine learning techniques to develop a model of schizophrenia that identifies the connections in the brain most associated with the illness.

New Study Demonstrates Potential for AI and Whole Genome Sequencing to Scale Access to Precision Medicine

From IBM News room – 2017-07-11 Study by New York Genome Center and IBM Demonstrates Potential for AI and Whole Genome Sequencing to Scale Access to Precision Medicine – United States

researchers at the New York Genome Center (NYGC), The Rockefeller University and other NYGC member institutions, and IBM (NYSE: IBM) bhave illustrated the potential of IBM Watson for Genomics to analyze complex genomic data from state-of-the-art DNA sequencing of whole genomes. The study compared multiple techniques – or assays – used to analyze genomic data from a glioblastoma patient’s tumor cells and normal healthy cells.

The proof of concept study used a beta version of Watson for Genomics technology to help interpret whole genome sequencing (WGS) data for one patient. In the study, Watson was able to provide a report of potential clinically actionable insights within 10 minutes, compared to 160 hours of human analysis and curation required to arrive at similar conclusions for this patient.

Comparing sequencing assays and human-machine analyses in actionable genomics for glioblastoma

From Comparing sequencing assays and human-machine analyses in actionable genomics for glioblastoma

Objective: To analyze a glioblastoma tumor specimen with 3 different platforms and compare potentially actionable calls from each.

Methods: Tumor DNA was analyzed by a commercial targeted panel. In addition, tumor-normal DNA was analyzed by whole-genome sequencing (WGS) and tumor RNA was analyzed by RNA sequencing (RNA-seq). The WGS and RNA-seq data were analyzed by a team of bioinformaticians and cancer oncologists, and separately by IBM Watson Genomic Analytics (WGA), an automated system for prioritizing somatic variants and identifying drugs.

Results: More variants were identified by WGS/RNA analysis than by targeted panels. WGA completed a comparable analysis in a fraction of the time required by the human analysts.

Conclusions: The development of an effective human-machine interface in the analysis of deep cancer genomic datasets may provide potentially clinically actionable calls for individual patients in a more timely and efficient manner than currently possible.