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Using AI to prevent harm caused by immunotherapy

    Researchers at Case Western Reserve University, using artificial intelligence (AI) to analyze simple tissue scans, state that they have discovered biomarkers that could tell doctors which lung cancer patients might actually get worse from immunotherapy.

    Using Artificial Intelligence to prevent harm caused by immunotherapyUntil recently, researchers and oncologists had placed these lung cancer patients into two broad categories: those who would benefit from immunotherapy, and those who likely would not.

AI debating system able to compete with expert human debaters

    IBM has developed an artificial intelligence-based system designed to engage in debates with humans. In their paper published in the journal Nature, the team members describe their system and how well it performed when pitted against human opponents.

    Debating is a skill humans have been honing for thousands of years. It is generally considered to be a type of discussion in which one or more people attempt to persuade others that their opinion on a topic is right. IBM has created an AI system designed to debate with humans in a live setting.

IBM quantum computing development roadmap envisions applications running 100 times faster

    IBM has been among the most aggressive in trying to build momentum for quantum computing. In 2016, the company launched the Q Network, which allows companies to begin experimenting with quantum computers via the company’s cloud service. According to Sutor, the Q Network now has more than 135 organizations, including corporations such as JP Morgan Chase and Exxon, as well as universities and startups.

    According to IBM’s quantum hardware roadmap, the company expects to achieve 100 qubits (the measure of a quantum computer’s processing power) this year, 400 qubits next year, and 1,000 qubits by 2023. While there are still major scientific hurdles to clear to make quantum computing superior to classical computing, Sutor said IBM is in a strong position to overcome them.

AI and Life Sciences: Has protein folding been solved?

    Amongst the bad news that took up a lot of headlines last year, there was one story at the end of last year that caused a lot of excitement in the life sciences sector. DeepMind’s Artificial Intelligence – AlphaFold 2 – appears to have solved the conundrum of protein folding.

    The technology to read (and indeed edit) DNA sequences and thus the amino acids sequences that they encode has developed rapidly over the past few decades. However, predicting exactly how amino acid sequences then fold into the complex three dimensional (3D) structures of proteins has, so far, not been possible. The 3D structure of a protein is critical to its biological activity. To identify a protein’s 3D structure, it has been necessary to utilise complex and expensive experimental methods (such as X-ray crystallography). This has resulted in a vast chasm between the number of known DNA and amino-acid sequences encoding proteins and the number of known 3D protein structures they encode. The ability to actually predict a protein structure has been thought to be a problem too complex to solve with current technology. Enter artificial intelligence.


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