Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

Random graphs

BIOE 402. Medical Technology Assessment. 2 or 3 hours. Bioentrepreneur course. Assessment of medical technology in the context of commercialization. Objectives, competition, market share, funding, pricing, manufacturing, growth, and intellectual property; many issues unique to biomedical products. Course Information: 2 undergraduate hours. 3 graduate hours. Prerequisite(s): Junior standing or above and consent of the instructor.

“Bioinformatics” 특집을 내면서

The cultural origins of human cognition

The computational brain: Patricia S. Churchland and Terrence J. Sejnowski (MIT Press, Cambridge, MA, 1992); xi, 544 pages, $39.95

Multicore platforms are emerging trends in the design of System-on-Chips (SoCs). Interconnect fabrics for these multicore SoCs play a crucial role in achieving the target performance. The Network-on-Chip (NoC) paradigm has been proposed as a promising solution for designing the interconnect fabric of multicore SoCs. But the performance requirements of NoC infrastructures in future technology nodes cannot be met by relying only on material innovation with traditional scaling. The continuing demand for low-power and high-speed interconnects with technology scaling necessitates looking beyond the conventional planar metal/dielectric-based interconnect infrastructures. Among different possible alternatives, the on-chip wireless communication network is envisioned as a revolutionary methodology, capable of bringing significant performance gains for multicore SoCs. Wireless NoCs (WiNoCs) can be designed by using miniaturized on-chip antennas as an enabling technology. In this paper, we present design methodologies and technology requirements for scalable WiNoC architectures and evaluate their performance. It is demonstrated that WiNoCs outperform their wired counterparts in terms of network throughput and latency, and that energy dissipation improves by orders of magnitude. The performance of the proposed WiNoC is evaluated in presence of various traffic patterns and also compared with other emerging alternative NoCs.

/pdf/scalable-hybrid-wireless-network-on-chip-architectures-for-5fa4vdmh52.pdf

Scalable Hybrid Wireless Network-on-Chip Architectures for Multicore Systems

We propose a computational model of the emergence of gaze following skills in infant-caregiver interactions. The model is based on the idea that infants learn that monitoring their caregiver's direction of gaze allows them to predict the locations of interesting objects or events in their environment (Moore & Corkum, 1994). Elaborating on this theory, we demonstrate that a specific Basic Set of structures and mechanisms is sufficient for gaze following to emerge. This Basic Set includes the infant's perceptual skills and preferences, habituation and reward-driven learning, and a structured social environment featuring a caregiver who tends to look at things the infant will find interesting. We review evidence that all elements of the Basic Set are established well before the relevant gaze following skills emerge. We evaluate the model in a series of simulations and show that it can account for typical development. We also demonstrate that plausible alterations of model parameters, motivated by findings on two different developmental disorders - autism and Williams syndrome - produce delays or deficits in the emergence of gaze following. The model makes a number of testable predictions. In addition, it opens a new perspective for theorizing about cross-species differences in gaze following.

/pdf/gaze-following-why-not-learn-it-h0ytborvbu.pdf

Gaze following: why (not) learn it?

Foreword (D. Hofstaedter) - Preface (Ch. Teuscher) PART I: TURING'S LIFE AND THOUGHTS Alan's Life: A Short Biography (A. Hodges) - Hacking the Turing Test (V. Paterna) - From Turing to the Information Society (D. Cerqui) PART II: COMPUTATION AND TURING MACHINES The Mechanization of Mathematics (M.J. Beeson) - Hypercomputational Models (M. Stannett) - Turing's Ideas and Models of Computation (E. Eberbach, D. Goldin, P. Wegner) - The Myth of Hypercomputation (M. Davis) - Quantum Computers: The Church-Turing Hypothesis versus the Turing Principle (Ch.G. Timpson) - Implementation of a Self-Replicating Universal Turing Machine (H.F. Restrepo, D. Mange, G. Tempesti) - Cognitive Science and the Turing Machine: An Ecological Perspective (A.J. Wells) PART III: ARTIFICIAL INTELLIGENCE AND THE TURING TEST Can Machines Think? (D.C. Dennett) - The Law of Accelerating Returns (R. Kurzweil) - The Computer, Artificial Intelligence, and the Turing Test (B.J. Copeland, D. Proudfoot) - Robots and Rule-Following (D. Proudfoot) - Strawberries with Cream, Mistakes, and Other Idiotic Features (Helmut Schnelle) PART IV: THE ENIGMA Alan M. Turing's Contributions to Co-Operation between the UK and the US (L.A. Gladwin) - The Brains behind the Enigma Code Breaking before the Second World War (E. Rakus-Andersson) - Alan Turing at Bletchley Park in World War II (T. Sale) PART V: ALMOST FORGOTTEN IDEAS Turing's Connectionism (Ch. Teuscher) - Watching the Daisies Grow: Turing and Fibonacci Phyollotaxis (J. Swinton) - What would Alan Turing have done after 1954? (A. Hodges)

Alan Turing: Life and Legacy of a Great Thinker

As feature-size scaling and "Moore's Law" in integrated CMOS circuits further slows down, attention is shifting to computing by non-von Neumann and non-Boolean computing models. Reservoir computing (RC) is a new computing paradigm that allows to harness the intrinsic dynamics of a "reservoir" to perform useful computations. The reservoir, or compute core, must only provide sufficiently rich dynamics that are then mapped onto a low-dimensional space by an readout layer. One of the key advantages of this approach is that only the readout layer needs to be adapted to perform the desired computation. The reservoir itself remains unchanged. In this paper we use for the first time memristive components as reservoir building blocks that are assembled into device networks. Memristive components are particularly interesting for this purpose because of their non-linear and memory characteristics. In addition, they can be integrated very densely and provide rich dynamics with a few components only. We use pattern recognition and associative memory tasks to illustrate the memristive reservoir computing approach. For that purpose, we have built a software framework that allows to create valid memristor networks, to simulate and evaluate them in Ngspice, and to train the readout layer by means of a Genetic Algorithm (GA). Our results show that we can efficiently and robustly classify temporal patterns. The approach presents a promising new computing paradigm that harnesses the non-linear, time-dependent, and highly-variable properties of current memristive components for solving computational tasks.

/pdf/memristor-based-reservoir-computing-1ibf6dvr7r.pdf

Memristor-based reservoir computing

Reconfigurable devices offer the ability to program electronic circuits on demand. In this work, we demonstrated on-demand creation of artificial neurons, synapses, and memory capacitors in post-fabricated perovskite NdNiO3 devices that can be simply reconfigured for a specific purpose by single-shot electric pulses. The sensitivity of electronic properties of perovskite nickelates to the local distribution of hydrogen ions enabled these results. With experimental data from our memory capacitors, simulation results of a reservoir computing framework showed excellent performance for tasks such as digit recognition and classification of electrocardiogram heartbeat activity. Using our reconfigurable artificial neurons and synapses, simulated dynamic networks outperformed static networks for incremental learning scenarios. The ability to fashion the building blocks of brain-inspired computers on demand opens up new directions in adaptive networks. Description Reconfigurable neuromorphic functions Having all the core functionality required for neuromorphic computing in one type of a device could offer dramatic improvements to emerging computing architectures and brain-inspired hardware for artificial intelligence. Zhang et al. showed that proton-doped perovskite neodymium nickelate (NdNiO3) could be reconfigured at room temperature by simple electrical pulses to generate the different functions of neuron, synapse, resistor, and capacitor (see the Perspective by John). The authors designed a prototype experimental network that not only demonstrated electrical reconfiguration of the device, but also showed that such dynamic networks enabled a better approximation of the dataset for incremental learning scenarios compared with static networks. —YS Hydrogen-doped perovskites can be reconfigured by electrical pulses to take on all essential functions necessary for artificial intelligence hardware.

https://www.science.org/doi/pdf/10.1126/science.abj7943?download=true

Christof Teuscher

Papers

Scalable Hybrid Wireless Network-on-Chip Architectures for Multicore Systems

Gaze following: why (not) learn it?

Alan Turing: Life and Legacy of a Great Thinker

Memristor-based reservoir computing

Reconfigurable perovskite nickelate electronics for artificial intelligence