Building an Artificial Intelligence Laboratory Based on Real World Data: The Experience of Gemelli Generator

The problem of transforming Real World Data into Real World Evidence is becoming increasingly important in the frameworks of Digital Health and Personalized Medicine, especially with the availability of modern algorithms of Artificial Intelligence high computing power, and large storage facilities.Even where Real World Data are well maintained in a hospital data warehouse and are made available for research purposes, many aspects need to be addressed to build an effective architecture enabling researchers to extract knowledge from data.We describe the first year of activity at Gemelli Generator RWD, the challenges we faced and the solutions we put in place to build a Real World Data laboratory at the service of patients and health researchers. Three classes of services are available today: retrospective analysis of existing patient data for descriptive and clustering purposes; automation of knowledge extraction, ranging from text mining, patient selection for trials, to generation of new research hypotheses; and finally the creation of Decision Support Systems, with the integration of data from the hospital data warehouse, apps, and Internet of Things.

The breakthrough in protein structure prediction

Proteins are the essential agents of all living systems. Even though they are synthesized as linear chains of amino acids, they must assume specific three-dimensional structures in order to manifest their biological activity. These structures are fully specified in their amino acid sequences — and therefore in the nucleotide sequences of their genes. However, the relationship between sequence and structure, known as the protein folding problem, has remained elusive for half a century, despite sustained efforts. To measure progress on this problem, a series of doubly blind, biennial experiments called CASP (critical assessment of structure prediction) were established in 1994. We were part of the assessment team for the most recent CASP experiment, CASP14, where we witnessed an astonishing breakthrough by DeepMind, the leading artificial intelligence laboratory of Alphabet Inc. The models filed by DeepMind's structure prediction team using the program AlphaFold2 were often essentially indistinguishable from experimental structures, leading to a consensus in the community that the structure prediction problem for single protein chains has been solved. Here, we will review the path to CASP14, outline the method employed by AlphaFold2 to the extent revealed, and discuss the implications of this breakthrough for the life sciences.

Smart collections

This paper reports on a joint proof of concept project undertaken by researchers from the Flinders University Artificial Intelligence Laboratory in partnership with information managers from the Education Network Australia (edna) team at Education Services Australia to address the question of whether artificial intelligence techniques could be employed to help with creation and consistency of learning resource metadata and improve the efficiency of digital collection workflows? The results show some success with automated subject categorisation on a small sample, and the researchers conclude that automated classification based on artificial intelligence is useful as a means of supplementing and assisting human classification, but is not at this stage a replacement for human classification of educational resources.

The Pransky interview: Dr Aaron Edsinger, CEO and cofounder at Hello Robot Inc

Purpose The purpose of this paper is to present a “Q&A interview” conducted by Joanne Pransky of Industrial Robot Journal as a method to impart the combined technological, business and personal experience of a prominent, robotic industry PhD-turned entrepreneur regarding the evolution, commercialization and challenges of bringing a technological invention to market. Design/methodology/approach The interviewee is Dr Aaron Edsinger, a proven entrepreneur and inventor in the field of human-collaborative robotics. Dr Edsinger shares his journey that led him from developing humanoids at Rodney Brooks’ Computer Science and Artificial Intelligence Laboratory at MIT, to cofounding four companies, two of which got purchased by Google. Findings Dr Edsinger received a BS degree in Computer Systems Engineering from Stanford, an MS in Computer Science from the Massachusetts Institute of Technology (MIT) and a PhD in Computer Science from MIT and did post-doctorate research in the Humanoid Robotics Group at the MIT Computer Science and Artificial Intelligence Lab. He co-founded his first company Meka Robotics in 2007 and that same year, he started his second company, HStar Technologies. In 2011, he cofounded Redwood Robotics, and in 2013, he sold Meka and Redwood to Google. From 2013 to 2017, he was a Robotics Director at Google. In August of 2017, he cofounded Hello Robot Inc. Originality/value Dr Edsinger’s work in robotics grew out of the San Francisco robotic art scene in the 1990s. Since then, he has collaborated and built over a dozen research and artistic robot platforms and has been granted 28 patents. His world-class robotic systems encompass Dr Edsinger’s innovative research in dexterous manipulation in unstructured environments, force controlled compliant actuation, human safe robotics, integrated mechatronic engineering and the design of humanoid robots. Domo, the humanoid robot he built, was named one of Time magazine’s Best Inventions of the Year for 2007. Out of the eight robot companies Google purchased in 2013, two were cofounded by Dr Edsinger. In 2017, Dr Edsinger left Google to cofound his new company, Hello Robot Inc, a stealth mode consumer robot company.

Research in Progress at the Massachusetts Institute of Technology Artificial Intelligence Laboratory

The MIT AI Laboratory has a long tradition of research in most aspects of Artificial Intelligence. Currently, the major foci include computer vision, manipulation, learning, English-language understanding, VLSI design, expert engineering problem solving, common-sense reasoning, computer architecture, distributed problem solving, models of human memory, programmer apprentices, and human education.

Acts of Perception: Samuel Becket, Time, Space and the Digital Literary Atlas of Ireland, 1922–1949

Situated in the wake of the first and second waves of the Digital Humanities, the Digital Literary Atlas of Ireland, 1922–1949 website provides interactive mapping and timeline features for academics and members of the public who are interested in the intersection of Irish literary culture, history, and environment. The site hosts Google Earth software produced interfaces with the EXHIBIT Timeline functions made available by the Semantic Interoperability of Metadata and Information in unLike Environments (SIMILE) project, developed and hosted by the Massachusetts Institute of Technology's (MIT) Computer Science and Artificial Intelligence Laboratory (CSAIL) and Library. This paper's case study maps the biographical lifepath of the writer Samuel Beckett using digital humanities techniques such as ergodicity, and deformance. The geo-digital-timeline mapping of his biography allows us to visualize the shift in Beckett's literary perspective from a latent Cartesian verisimilitude to more phenomenological and fragmented, existential impressions of time and place. The atlas's visualizations of his Wanderjahre years in various European metropoles chart the intellectual and aesthetic influences shaping the Beckettian literary landscapes of his later and better-known works, such as En Attendant Godot (1953). Beckett's thought, works, and shifts in perception provide insight into how digital cultural mapping practices and third wave digital humanities methodologies and tools can be conceptualized and operationalized.

The Pransky interview: Dr Rodney Brooks, Robotics Entrepreneur, Founder and CTO of Rethink Robotics

Purpose – This article, a “Q&A interview” conducted by Joanne Pransky of Industrial Robot Journal, aims to impart the combined technological, business, and personal experience of a prominent, robotic industry engineer-turned entrepreneur regarding the evolution, commercialization, and challenges of bringing a technological invention to market. Design/methodology/approach – The interviewee is Dr Rodney Brooks, the Panasonic Professor of Robotics (emeritus), Massachusetts Institute of Technology (MIT), Computer Science and Artificial Intelligence Lab; Founder, Chief Technical Officer (CTO) and Chairman of Rethink Robotics. Dr Brooks shares some of his underlying principles in technology, academia and business, as well as past and future challenges. Findings – Dr Brooks received degrees in pure mathematics from the Flinders University of South Australia and a PhD in computer science from Stanford University in 1981. He held research positions at Carnegie Mellon University and MIT, and a faculty position at Stanford before joining the faculty of MIT in 1984. He is also a Founder, Board Member and former CTO (1991-2008) of iRobot Corp (Nasdaq: IRBT). Dr Brooks is the former Director (1997-2007) of the MIT Artificial Intelligence Laboratory and then the MIT Computer Science & Artificial Intelligence Laboratory. He founded Rethink Robotics (formerly Heartland Robotics) in 2008. Originality/value – While at MIT, in 1988, Dr Brooks built Genghis, a hexapodal walker, designed for space exploration (which was on display for ten years in the Smithsonian National Air and Space Museum in Washington, D.C.). Genghis was one of the first robots that utilized Brooks’ pioneering subsumption architecture. Dr Brooks’ revolutionary behavior-based approach underlies the autonomous robots of iRobot, which has sold more than 12 million home robots worldwide, and has deployed more than 5,000 defense and security robots; and Rethink Robotics’ Baxter, the world’s first interactive production robot. Dr Brooks has won the Computers and Thought Award at the 1991 International Joint Conference on Artificial Intelligence, the 2008 IEEE Inaba Technical Award for Innovation Leading to Production, the 2014 Robotics Industry Association’s Engelberger Robotics Award for Leadership and the 2015 IEEE Robotics and Automation Award.

Life and Times of the Samson Box

Peter Samson designed and built a real-time signal-processing computer for music applications in the 1970s. The Systems Concepts Digital Synthesizer (“Samson Box” for short) was installed at the Center for Computer Research in Music and Acoustics (CCRMA) at Stanford University in 1977, where it served for over a decade as the principal music generation system. It was an important landmark in the transition from general-purpose computers to real-time systems for music and audio, and helped set the stage for the sea change in the music industry from analog to digital technologies that began in the 1980s and continues at a rapid pace today. This article focuses on the historical context of the Samson Box, its development, its impact on the culture of CCRMA and the Stanford Artificial Intelligence Laboratory, its use for music research and composition at Stanford, and its role in the transformation of the music and audio industries from analog to digital practices. A list of compositions realized on the Samson Box is included, which shows that from 1978 to its decommissioning in 1992 it was used to create over 100 finished works, many of which were widely performed and were awarded prizes. A companion article provides a detailed architectural review and an interview with Pete Samson.