Artificial intelligence marketing (AIM) is a form of direct marketing leveraging database marketing techniques as well as AI concept and model such as machine learning and Bayesian Network. The main difference resides in the reasoning part which suggests it is performed by computer and algorithm instead of human
Ignore today’s small incremental advancements in artificial intelligence, such as the enhancing capabilities of automobiles to drive themselves. Waiting in the wings could be a groundbreaking growth: a machine that knows itself as well as its environments, which might take in and also procedure huge amounts of data in real time. It could be sent on harmful goals, right into room or fight. Along with driving people about, it might be able to prepare, tidy, do washing– as well as keep people company when other people typically aren’t close by.
A specifically innovative set of machines might replace people at essentially all jobs. That would save humankind from workaday grind, yet it would certainly likewise tremble several societal foundations. A life of no job and also only play might turn out to be a dystopia.
Mindful equipments would additionally elevate troubling lawful as well as honest troubles. Would certainly a mindful machine be a “person” under regulation as well as be responsible if its actions hurt someone, or if something goes wrong? To consider an extra frightening circumstance, might these machines rebel against human beings and also desire to eliminate us completely? If yes, they represent the culmination of advancement.
As a teacher of electric design and computer science that operates in artificial intelligence and also quantum theory, I can state that scientists are split on whether these type of hyperaware machines will certainly ever before exist. There’s additionally dispute regarding whether machines might or ought to be called “aware” in the method we think about human beings, as well as some pets, as aware. A few of the questions pertain to innovation; others relate to what consciousness in fact is.
Is Recognition Sufficient? Many computer researchers assume that awareness is a particular that will certainly emerge as innovation develops. Some believe that awareness includes approving brand-new information, saving as well as fetching old info and cognitive processing of all of it right into assumptions and activities. If that’s right, then one day makers will certainly undoubtedly be the best consciousness. They’ll be able to collect even more information than a human, shop more than numerous collections, accessibility substantial databases in milliseconds and also calculate all of it right into decisions extra complex, and yet a lot more sensible, than any person ever before could.
On the various other hand, there are physicists and also thinkers who claim there’s something more regarding human habits that can not be calculated by a maker. Creative thinking, for example, and also the sense of freedom individuals possess don’t show up ahead from logic or calculations.
Yet these are not the only sights of what consciousness is, or whether devices could ever before accomplish it.
Quantum Views One more perspective on awareness comes from quantum theory, which is the inmost theory of physics. Inning accordance with the orthodox Copenhagen Analysis, awareness as well as the real world are corresponding aspects of the very same fact. When a person observes, or experiments on, some aspect of the physical world, that individual’s conscious interaction causes noticeable change. Considering that it takes consciousness as a given as well as no effort is made to derive it from physics, the Copenhagen Analysis may be called the “big-C” view of consciousness, where it is a thing that exists on its own– although it needs brains to come to be real. This view was prominent with the pioneers of quantum concept such as Niels Bohr, Werner Heisenberg and Erwin Schrodinger.
The communication in between consciousness and also issue leads to paradoxes that remain unsolved after 80 years of argument. A widely known example of this is the mystery of Schrodinger’s cat, where a pet cat is placed in a scenario that results in it being similarly likely to endure or pass away– and the act of monitoring itself is what makes the end result certain.
The opposing view is that awareness emerges from biology, just as biology itself emerges from chemistry which, consequently, emerges from physics. We call this less extensive concept of consciousness “little-C.” It concurs with the neuroscientists’ sight that the processes of the mind correspond states and processes of the brain. It also agrees with a more recent analysis of quantum theory encouraged by an effort to clear it of mysteries, the Several Worlds Interpretation, where observers belong of the mathematics of physics.
Theorists of science believe that these modern-day quantum physics sights of consciousness have parallels in old philosophy. Big-C resembles the theory of mind in Vedanta– in which consciousness is the essential basis of truth, on the same level with the physical world.
Little-C, on the other hand, is fairly much like Buddhism. Although the Buddha chose not to address the inquiry of the nature of awareness, his fans stated that mind and also consciousness arise out of emptiness or nothingness.
Big-C and Scientific Exploration Researchers are additionally exploring whether awareness is constantly a computational process. Some scholars have actually suggested that the imaginative moment is not at the end of a calculated computation. For example, fantasizes or visions are intended to have influenced Elias Howe’s 1845 style of the contemporary embroidery device, as well as August Kekule’s exploration of the framework of benzene in 1862.
A dramatic item of proof in favor of big-C consciousness existing all on its own is the life of self-taught Indian mathematician Srinivasa Ramanujan, who died in 1920 at the age of 32. His note pad, which was lost as well as neglected for regarding 50 years and published just in 1988, contains a number of thousand formulas, without evidence in various areas of mathematics, that were well ahead of their time. Moreover, the methods whereby he located the solutions remain elusive. He himself declared that they were revealed to him by a siren while he was asleep.
The principle of big-C consciousness increases the inquiries of exactly how it relates to matter, as well as how matter and also mind mutually affect each other. Awareness alone can not make physical modifications to the world, yet maybe it could change the probabilities in the advancement of quantum procedures. The act of monitoring can freeze and even affect atoms’ motions, as Cornell physicists verified in 2015. This might quite possibly be an explanation of exactly how matter and mind engage.
Mind and Self-Organizing Solutions It is feasible that the sensation of consciousness calls for a self-organizing system, like the brain’s physical framework. If so, then present makers will come up short.
Scholars aren’t sure if adaptive self-organizing equipments could be designed to be as advanced as the human mind; we lack a mathematical theory of calculation for systems like that. Probably it holds true that just biological machines could be sufficiently imaginative and adaptable. However then that recommends individuals ought to– or quickly will certainly– begin working on engineering brand-new biological frameworks that are, or could end up being, mindful.
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I was asked the question by the head of a new startup for AI as their technology aims to change the world I quote. Will jobs done by regular people be replaced? Fast Company predicts these will be the jobs that will be the worst hit. 1. INSURANCE UNDERWRITERS AND CLAIMS REPRESENTATIVES 2. BANK TELLERS […]
Artificial intelligence (AI, also machine intelligence, MI) is intelligence exhibited by machines, rather than humans or other animals (natural intelligence, NI). In computer science, the field of AI research defines itself as the study of “intelligent agents”: any device that perceives its environment and takes actions that maximize its chance of success at some goal. Colloquially, the term “artificial intelligence” is applied when a machine mimics “cognitive” functions that humans associate with other human minds, such as “learning” and “problem solving”.
The scope of AI is disputed: as machines become increasingly capable, tasks considered as requiring “intelligence” are often removed from the definition, a phenomenon known as the AI effect, leading to the quip “AI is whatever hasn’t been done yet.” For instance, optical character recognition is frequently excluded from “artificial intelligence”, having become a routine technology. Capabilities generally classified as AI, as of 2017, include successfully understanding human speech, competing at a high level in strategic game systems (such as chess and Go), autonomous cars, intelligent routing in content delivery networks, military simulations, and interpreting complex data.
Artificial intelligence was founded as an academic discipline in 1956, and in the years since has experienced several waves of optimism, followed by disappointment and the loss of funding (known as an “AI winter”), followed by new approaches, success and renewed funding. For most of its history, AI research has been divided into subfields that often fail to communicate with each other. However, in the early 21st century statistical approaches to machine learning became successful enough to eclipse all other tools, approaches, problems and schools of thought.
The traditional problems (or goals) of AI research include reasoning, knowledge, planning, learning, natural language processing, perception and the ability to move and manipulate objects. General intelligence is among the field’s long-term goals. Approaches include statistical methods, computational intelligence, and traditional symbolic AI. Many tools are used in AI, including versions of search and mathematical optimization, neural networks and methods based on statistics, probability and economics. The AI field draws upon computer science, mathematics, psychology, linguistics, philosophy, neuroscience, artificial psychology and many others.
The field was founded on the claim that human intelligence “can be so precisely described that a machine can be made to simulate it”. This raises philosophical arguments about the nature of the mind and the ethics of creating artificial beings endowed with human-like intelligence, issues which have been explored by myth, fiction and philosophy since antiquity. Some people also consider AI a danger to humanity if it progresses unabatedly.
In the twenty-first century, AI techniques have experienced a resurgence following concurrent advances in computer power, large amounts of data, and theoretical understanding, and AI techniques have become an essential part of the technology industry, helping to solve many challenging problems in computer science.
I used to have a description of each of my papers on this page, but it got very boring to read as the numbers grew, so I moved most of it to here. After graduate work on the role of atomic and molecular chemistry in cosmic reionization, I have mainly focused my research on issues related to constraining cosmological models. A suite of papers developed methods for analyzing cosmological data sets and applied them to various CMB experiments and galaxy redshift surveys, often in collaboration with the experimentalists who had taken the data. Another series of papers tackled various “dirty laundry” issues such as microwave foregrounds and mass-to-light bias. Other papers like this one develop and apply techniques for clarifying the big picture in cosmology: comparing and combining diverse cosmological probes, cross-checking for consistency and constraining cosmological models and their free parameters. (The difference between cosmology and ice hockey is that I don’t get penalized for cross-checking…) My main current research interest is cosmology theory and phenomenology. I’m particularly enthusiastic about the prospects of comparing and combining current and upcoming data on CMB, LSS, galaxy clusters, lensing, LyA forest clustering, SN 1, 21 cm tomography, etc. to raise the ambition level beyond the current cosmological parameter game, testing rather than assuming the underlying physics. This paper contains my battle cry. I also retain a strong interest in low-level nuts-and-bolts analysis and interpretation of data, firmly believing that the devil is in the details, and am actively working on neutral hydrogen tomography theory, experiment and data analysis for our Omniscope project, which you can read all about here.
OTHER RESEARCH: SIDE INTERESTS Early galaxy formation and the end of the cosmic dark ages One of the main challenges in modern cosmology is to quantify how small density fluctuations at the recombination epoch at redshift around z=1000 evolved into the galaxies and the large-scale structure we observe in the universe today. My Ph.D. thesis with Joe Silk focused on ways of probing the interesting intermediate epoch. The emphasis was on the role played by non-linear feedback, where a small fraction of matter forming luminous objects such as stars or QSO’s can inject enough energy into their surrounding to radically alter subsequent events. We know that the intergalactic medium (IGM) was reionized at some point, but the details of when and how this occurred remain open. The absence of a Gunn-Peterson trough in the spectra of high-redshift quasars suggests that it happened before z=5, which could be achieved through supernova driven winds from early galaxies. Photoionization was thought to be able to partially reionize the IGM much earlier, perhaps early enough to affect the cosmic microwave background (CMB) fluctuations, especially in an open universe. However, extremely early reionization is ruled out by the COBE FIRAS constraints on the Compton y-distortion. To make predictions for when the first objects formed and how big they were, you need to worry about something I hate: molecules. Although I was so fed up with rate discrepancies in the molecule literature that I verged on making myself a Ghostbuster-style T-shirt reading “MOLECULES – JUST SAY NO”, the irony is that my molecule paper that I hated so much ended up being one of my most cited ones. Whereas others that I had lots of fun with went largely unnoticed…
Math problemsI’m also interested in physics-related mathematics problems in general. For instance, if you don’t believe that part of a constrained elliptic metal sheet may bend towards you if you try to push it away, you are making the same mistake that the famous mathematician Hadamard once did.
WELCOME TO MY TECHNICAL UNIVERSE I love working on projects that involve cool questions, great state-of-the-art data and powerful physical/mathematical/computational tools. During my first quarter-century as a physics researcher, this criterion has lead me to work mainly on cosmology and quantum information. Although I’m continuing my cosmology work with the HERA collaboration, the main focus of my current research is on the physics of cognitive systems: using physics-based techniques to understand how brains works and to build better AI (artificial intelligence) systems. If you’re interested in working with me on these topics, please let me know, as I’m potentially looking for new students and postdocs (see requirements). I’m fortunate to have collaborators who generously share amazing neuroscience data with my group, including Ed Boyden, Emery Brown and Tomaso Poggio at MIT and Gabriel Kreimann at Harvard, and to have such inspiring colleagues here in our MIT Physics Department in our new division studying the physics of living systems. I’ve been pleasantly surprised by how many data analysis techniques I’ve developed for cosmology can be adapted to neuroscience data as well. There’s clearly no shortage of fascinating questions surrounding the physics of intelligence, and there’s no shortage of powerful theoretical tools either, ranging from neural network physics and non-equilibrium statistical mechanics to information theory, the renormalization group and deep learning. Intriguingly and surprisingly, there’s a duality between the last two. I recently helped organize conferences on the physics of information and artificial intelligence. I’m very interested in the question of how to model an observer in physics, and if simple necessary conditions for a physical system being a conscious observer can help explain how the familiar object hierarchy of the classical world emerges from the raw mathematical formalism of quantum mechanics. Here’s a taxonomy of proposed consciousness measures. Here’s a TEDx-talk of mine about the physics of consciousness. Here’s an intriguing connection between critical behavior in magnets, language, music and DNA. In older work of mine on the physics of the brain, I showed that neuron decoherence is way too fast for the brain to be a quantum computer. However, it’s nonetheless interesting to study our brains as quantum systems, to better understand why they perceives the sort of classical world that they do. For example, why do we feel that we live in real space rather than Fourier space, even though both are equally valid quantum descriptions related by a unitary transformation?
Quantum information My work on the physics of cognitive systems is a natural outgrowth of my long-standing interest in quantum information, both for enabling new technologies such as quantum computing and for shedding new light on how the world fundamentally works. For example, I’m interested in how the second law of thermodynamics can be generalized to explain how the entropy of a system typically decreases while you observe a system and increases while you don’t, and how this can help explain how inflation causes the emergence of an arrow of time. When you don’t observe an interacting system, you can get decoherence, which I had the joy of rediscovering as a grad student – if you’d like to know more about what this is, check out my article in with John Archibald Wheeler in Scientific American here. I’m interested in decoherence both for its quantitative implications for quantum computing etc and for its philosophical implications for the interpretation of quantum mechanics. For much more on this wackier side of mine, click the banana icon above. Since macroscopic systems are virtually impossible to isolate from their surroundings, a number of quantitative predictions can be made for how their wavefunction will appear to collapse, in good agreement with what we in fact observe. Similar quantitative predictions can be made for models of heat baths, showing how the effects of the environment cause the familiar entropy increase and apparent directionality of time. Intriguingly, decoherence can also be shown to produce generalized coherent states, indicating that these are not merely a useful approximation, but indeed a type of quantum states that we should expect nature to be full of. All these changes in the quantum density matrix can in principle be measured experimentally, with phases and all.
Cosmology My cosmology research has been focused on precision cosmology, e.g., combining theoretical work with new measurements to place sharp constraints on cosmological models and their free parameters. (Skip to here if you already know all this.) Spectacular new measurements are providing powerful tools for this:
So far, I’ve worked mainly on CMB, LSS and 21 cm tomography, with some papers involving lensing, SN Ia and LyAF as well. Why do I find cosmology exciting?(Even if you don’t find cosmology exciting, there are good reasons why you should support physics research.)
There are some very basic questions that still haven’t been answered. For instance,
Is really only 5% of our universe made of atoms? So it seems, but what precisely is the weird “dark matter” and “dark energy” that make up the rest?
Will the Universe expand forever or end in a cataclysmic crunch or big rip? The smart money is now on the first option, but the jury is still out.
How did it all begin, or did it? This is linked to particle physics and unifying gravity with quantum theory.
Are there infinitely many other stars, or does space connect back on itself? Most of my colleagues assume it is infinite and the data supports this, but we don’t know yet.
Thanks to an avalanche of great new data, driven by advances in satellite, detector and computer technology, we may be only years away from answering some of these questions.
Since our atmosphere messes up most electromagnetic waves coming from space (the main exceptions being radio waves and visible light), the advent of satellites has revolutionized our ability to photograph the Universe in microwaves, infrared light, ultraviolet light, X-rays and gamma rays. New low-temperature detectors have greatly improved what can be done from the ground as well, and the the computer revolution has enabled us to gather and process huge data quantities, doing research that would have been unthinkable twenty years ago. This data avalanche has transformed cosmology from being a mainly theoretical field, occasionally ridiculed as speculative and flaky, into a data-driven quantitative field where competing theories can be tested with ever-increasing precision. I find CMB, LSS, lensing, SN Ia, LyAF, clusters and BBN to be very exciting areas, since they are all being transformed by new high-precision measurements as described below. Since each of them measures different but related aspects of the Universe, they both complement each other and allow lots of cross-checks. What are these cosmological parameters? In our standard cosmological model, the Universe was once in an extremely dense and hot state, where things were essentially the same everywhere in space, with only tiny fluctuations (at the level of 0.00001) in the density. As the Universe expanded and cooled, gravitational instability caused these these fluctuations to grow into the galaxies and the large-scale structure that we observe in the Universe today. To calculate the details of this, we need to know about a dozen numbers, so-called cosmological parameters. Most of these parameters specify the cosmic matter budget, i.e., what the density of the Universe is made up of – the amounts of the following ingredients:
Baryons – the kind of particles that you and I and all the chemical elements we learned about in school are madeof : protons & neutrons. Baryons appear to make up only about 5% of all stuff in the Universe.
Photons – the particles that make uplight. Their density is the best measured one on this list.
Massive neutrinos – neutrinos are very shy particles. They are known to exist, and now at least two of the three or more kinds are known to have mass.
Cold dark matter – unseen mystery particles widely believed to exist. There seems to be about five times more of this strange stuff than baryons, making us a minority in the Universe.
Curvature – if the total density differs from a certain critical value, space will be curved. Sufficiently high density would make space be finite, curving back on itself like the 3D surface of a 4D hypersphere.
Dark energy – little more than a fancy name our ignorance of what seems to make up abouttwo thirdsof the matter budget. One popular candidates is a “Cosmological constant”, a.k.a. Lambda, which Einstein invented and then later called his greatest blunder. Other candidates are more complicated modifications toEinsteinstheory of Gravity as well as energy fields known as “quintessence”. Dark energy causes gravitational repulsion in place of attraction. Einstein invented it and called it his greatest mistake, but combining new SN Ia and CMB data indicates that we might be living with Lambda after all.
Then there are a few parameters describing those tiny fluctuations in the early Universe; exactly how tiny they were, the ratio of fluctuations on small and large scales, the relative phase of fluctuations in the different types of matter, etc. Accurately measuring these parameters would test the most popular theory for the origin of these wiggles, known as inflation, and teach us about physics at much higher energies than are accessible with particle accelerator experiments. Finally, there are a some parameters that Dick Bond, would refer to as “gastrophysics”, since they involve gas and other ghastly stuff. One example is the extent to which feedback from the first galaxies have affected the CMB fluctuations via reionization. Another example is bias, the relation between fluctuations in the matter density and the number of galaxies.One of my main current interests is using the avalanche of new data to raise the ambition level beyond cosmological parameters, testing rather than assuming the underlying physics. My battle cry is published here with nuts and bolts details here and here. The cosmic toolboxHere is a brief summary of some key cosmological observables and what they can teach us about cosmological parameters.
Photos of the cosmic microwave background (CMB) radiation like the one to the left show us the most distant object we can see: a hot, opaque wall of glowing hydrogen plasma about 14 billion light years away. Why is it there? Well, as we look further away, we’re seeing things that happened longer ago, since it’s taken the light a long time to get here. We see the Sun as it was eight minutes ago, the Andromeda galaxy the way it was a few million years ago and this glowing surface as it was just 400,000 years after the Big Bang. We can see that far back since the hydrogen gas that fills intergalactic space is transparent, but we can’t see further, since earlier the hydrogen was so hot that it was an ionized plasma, opaque to light, looking like a hot glowing wall just like the surface of the Sun. The detailed patterns of hotter and colder spots on this wall constitute a goldmine of information about the cosmological parameters mentioned above. If you are a newcomer and want an introduction to CMB fluctuations and what we can learn from them, I’ve written a review here. If you don’t have a physics background, I recommend the on-line tutorials by Wayne Hu and Ned Wright. Two new promising CMB fronts are opening up — CMB polarization and arcminute scale CMB, and are likely to keep the CMB field lively for at leastr another decade. Hydrogen tomography Mapping our universe in 3D by imaging the redshifted 21 cm line from neutral hydrogen has the potential to overtake the cosmic microwave background as our most powerful cosmological probe, because it can map a much larger volume of our Universe, shedding new light on the epoch of reionization, inflation, dark matter, dark energy, and neutrino masses. For this reason, my group built MITEoR, a pathfinder low-frequency radio interferometer whose goal was to test technologies that greatly reduce the cost of such 3D mapping for a given sensitivity. MITEoR accomplished this by using massive baseline redundancy both to enable automated precision calibration and to cut the correlator cost scaling from N2 to N log N, where N is the number of antennas. The success of MITEoR with its 64 dual-polarization elements bodes well for the more ambitious HERA project, which incorporates many of the technologies MITEoR tested using dramatically larger collecting area
. Large-scale structure: 3D mapping of the Universe with galaxy redshift surveys offers another window on dark matter properties, through its gravitational effects on galaxy clustering. This field is currently being transformed by everr larger Galaxy Redshift Survey. I’ve had lots of fun working with my colleagues on the Sloan Digital Sky Survey (SDSS) to carefully analyze the gargantuan galaxy maps and work out what they tell us about our cosmic composition, origins and ultimate fate. The abundance of galaxy clusters, the largest gravitationally bound and equilibrated blobs of stuff in the Universe, is a very sensitive probe of both the cosmic expansion history and the growth of matter clustering. Many powerful cluster finding techniques are contributing to rapid growth in the number of known clusters and our knowledge of their properties: identifying them in 3D galaxy surveys, seeing their hot gas as hot spots in X-ray maps or cold spots in microwave maps (the so-called SZ-effect) or spotting their gravitational effects with gravitational lensing. Yet another probe of dark matter is offered by gravitational lensing, whereby its gravitational pull bends light rays and distorts images of distant objects. The first large-scale detections of this effect were reported by four groups (astro-ph/0002500, 0003008, 0003014, 0003338) in the year 2000, and I anticipate making heavy use of such measurements as they continue to improve, partly in collaboration with Bhuvnesh Jain at Penn. Lensing is ultimately as promising as CMB and is free from the murky bias issues plaguing LSS and LyAF measurements, since it probes the matter density directly via its gravitational pull. I’ve also dabbled some in the stronger lensing effects caused by galaxy cores, which offer additional insights into the detailed nature of the dark matter.Supernovae Ia: If a white dwarf (the corpse of a burned-out low-mass star like our Sun) orbits another dying star, it may gradually steal its gas and exceed the maximum mass with which it can be stable. This makes it collapse under its own weight and blow up in a cataclysmic explosion called a supernova of type Ia. Since all of these cosmic bombs weigh the same when they go off (about 1.4 solar masses, the so-called Chandrasekhar mass), they all release roughly the same amount of energy – and a more detailed calibration of this energy is possible by measuring how fast it dims, making it the best “standard candle” visible at cosmological distances. The supernova cosmology project and the high z SN search team mapped out how bright SN Ia looked at different redshifts found the first evidence in 1998 that the expansion of the Universe was accelerating. This approach can ultimately provide a direct measurement of the density of the Universe as a function of time, helping unravel the nature of dark energy – I hope the SNAP project or one of its competitores gets funded. The image to the left resulted from a different type of supernova, but I couldn’t resist showing it anyway..
. The so-called Lyman Alpha Forest, cosmic gas clouds backlit by quasars, offers yet another new and exciting probe of how dark has clumped ordinary matter together, and is sensitive to an epoch when the Universe was merely 10-20% of its present age. Although relating the measured absorption to the densities of gas and dark matter involves some complications, it completely circumvents the Pandora’s of galaxy biasing. Cosmic observations are rapidly advancing on many other fronts as well, e.g., with direct measurements of the cosmic expansion rate and the cosmic baryon fraction.
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The Department of Electrical Engineering and Computer Science (EECS) has announced the appointment of two new associate department heads, and the creation of the new role of associate department head for strategic directions.
Professors Saman Amarasinghe and Joel Voldman have been named as new associate department heads, effective immediately, says EECS Department Head Asu Ozdaglar. Ozdaglar became department head on Jan. 1, replacing Anantha Chandrakasan, who is now dean of the School of Engineering. Professor Nancy Lynch will be the inaugural holder of the new position of associate department head for strategic directions, overseeing new academic and research initiatives.
“I am thrilled to be starting my own new role in collaboration with such a strong leadership team,” says Ozdaglar, who is also the Joseph F. and Nancy P. Keithley Professor of Electrical Engineering and Computer Science. “All three are distinguished scholars and dedicated educators whose experience will contribute greatly to shaping the department’s future.”
Saman Amarasinghe leads the Commit compiler research group at the Computer Science and Artificial Intelligence Laboratory (CSAIL). His group focuses on programming languages and compilers that maximize application performance on modern computing platforms. It has developed the Halide, TACO, Simit, StreamIt, StreamJIT, PetaBricks, MILK, Cimple, and GraphIt domain-specific languages and compilers, which all combine language design and sophisticated compilation techniques to deliver unprecedented performance for targeted application domains such as image processing, stream computations, and graph analytics.
Amarasinghe also pioneered the application of machine learning for compiler optimization, from Meta optimization in 2003 to OpenTuner extendable autotuner today. He was the co-leader of the Raw architecture project with EECS Professor and edX CEO Anant Agarwal. Recently, his work received a best-paper award at the 2017 Association for Computing Machinery (ACM) Object-Oriented Programming, Systems, Languages, and Applications (OOPSLA) conference and a best student-paper award at the 2017 Big Data conference.
Amarasinghe was the founder of Determina Inc., a startup based on computer security research pioneered in his MIT research group and later acquired by VMware. He is the faculty director for MIT Global Startup Labs, whose summer programs in 17 countries have helped launch more than 20 startups.
A faculty member since 1997, Amarasinghe served as an EECS education officer and currently chairs the department’s computer science graduate admissions committee. He developed the popular class 6.172 (Performance Engineering of Software Systems) with Charles Leiserson, the Edwin Sibley Webster Professor of EECS. Recently, he has created individualized software project classes such as the Open Source Software Project Lab, the Open Source Entrepreneurship Lab, and the Bring Your Own Software Project Lab.
He received a bachelor’s degree in EECS from Cornell University, and a master’s degree and PhD in electrical engineering from Stanford University. Amarasinghe succeeds Lynch, who had been an associate department head since September 2016.
Joel Voldmanis a professor in EECS and a principal investigator in the Research Laboratory of Electronics (RLE) and the Microsystems Technology Laboratories (MTL).
He received a bachelor’s degree in electrical engineering from the University of Massachusetts, Amherst, and SM and PhD degrees in electrical engineering from MIT. During his time at MIT, he developed biomedical microelectromechanical systems for single-cell analysis.
Afterward, he was a postdoctoral associate in George Church’s lab at Harvard Medical School, where he studied developmental biology. He returned to MIT as an assistant professor in EECS in 2001. He was awarded the NBX Career Development Chair in 2004, became an associate professor in 2006, and was promoted to professor in 2013.
Voldman’s research focuses on developing microfluidic technology for biology and medicine, with an emphasis on cell sorting and stem cell biology. He has developed a host of technologies to arrange, culture, and sort diverse cell types, including immune cells, endothelial cells, and stem cells. Current areas of research include recapitulating the induction of atherosclerosis on a microfluidic chip, and using microfluidic tools to study how immune cells decide to attack tumor cells. He is also interested in translational medical work, such as developing point-of-care drop-of-blood assays for proteins and rapid microfluidic tests for immune cell activation for the treatment of sepsis.
In addition, Voldman has co-developed two introductory EECS courses. One class, 6.03 (Introduction to EECS via Medical Technology), uses medical devices to introduce EECS concepts such as signal processing and machine learning. The other, more recent class, 6.S08/6.08 (Interconnected Embedded Systems ), uses the Internet of Things to introduce EECS concepts such as system partitioning, energy management, and hardware/software co-design.
Voldman’s awards and honors include a National Science Foundation (NSF) CAREER award, an American Chemical Society (ACS) Young Innovator Award, a Bose Fellow grant, MIT’s Jamieson Teaching Award, a Louis D. Smullin (’39) Award for Teaching Excellence from EECS, a Frank Quick Faculty Research Innovation Fellowship from EECS, an IEEE/ACM Best Advisor Award, and awards for posters and presentations at international conferences. Voldman succeeds Ozdaglar as ADH.
Nancy Lynch, the NEC Professor of Software Science and Engineering, also heads the Theory of Distributed Systems research group in CSAIL.
She is known for her fundamental contributions to the foundations of distributed computing. Her work applies a mathematical approach to explore the inherent limits on computability and complexity in distributed systems. Her best-known research is the FLP impossibility result for distributed consensus in the presence of process failures. Other research includes the I/O automata system modeling frameworks. Her recent work focuses on wireless network algorithms and biological distributed algorithms.
Lynch has written or co-written hundreds of research articles. She is the author of the textbook “Distributed Algorithms” and co-author of “Atomic Transactions” and “The Theory of Timed I/O Automata.” She is an ACM Fellow, a Fellow of the American Academy of Arts and Sciences, and a member of the National Academy of Science and the National Academy of Engineering. She has received the Dijkstra Prize twice, the van Wijngaarden prize, the Knuth Prize, the Piore Prize, and the Athena Prize.
A member of the MIT faculty since 1982, Lynch has supervised 30 PhD students and similar numbers of master’s-degree candidates and postdoctoral associates, many of whom have themselves become research leaders. She received a bachelor’s degree from Brooklyn College and a PhD from MIT, both in mathematics.
According to a new market report published by Credence Research “Plasticizers Market Growth, Future Prospects and Competitive Analysis, 2017 – 2025,” the plasticizers market is expected to reach over US$ 26.3 Bn by 2025, expanding at a CAGR of 5.4% from 2017 to 2025.
For the purpose of this study, the global plasticizers market is categorized into two product types viz., phthalates and non- phthalates. The market for plasticizers is segmented on the basis of sub-types such as Phthalates (Dioctyl Phthalate (Dop)/ Diethylhexyl Phthalate (Dehp), Diisononyl Phthalate (Dinp), Diisodecyl Phthalate (Didp), Di(2-Propylheptyl) Phthalate (Dphp), Butyl Benzyl Phthalate (Bbp), Others) and Non-Phthalates (Adipates, Esters, Trimellitates, Epoxy, Bio-Based Plasticizers, Dioctyl Terephthalate (Dotp), Others).
In 2016, the Phthalates segment dominated the global plasticizers market and will continue to dominate the market in upcoming years. The demand for phthalates is led by Dioctyl Phthalate (DOP), which accounted for a share of more than 40% of the overall market in 2016.
On the basis of application, the plasticizers market is segmented into Flooring & Wall, Film & Sheet Coverings, Wires & Cables, Coated Fabrics, Consumer Goods and Others (Medical, Sports, & Adhesive & Sealants Applications). Among these, wires and cables segment accounted for the largest segment by value in 2016 owing to high usage of plasticizers in electrical and cable industry for products such as insulation and jacketing for electrical conductors, insulation for fiber optic cables.
For the purpose of this study, the global plasticizers market is categorized into regional markets viz., North America, Europe, Asia Pacific and Latin America and Middle East and Africa. In base year 2016, Asia Pacific was observed as the largest market for plasticizers followed by North America and Europe. Asia Pacific is expected to achieve highest growth rate compared to other regions due to presence of countries such as China, India and Japan are observing significant industrial and construction growth.
Furthermore, the companies are focusing on expanding their business network, across regional markets. They are strengthening their market penetration by offering wide product range in the plasticizers segment. Aekyung Petrochemical Co. Ltd., Arkema S.A., BASF SE, Daelim Industrial Co. Ltd., Dow Chemical Company, Eastman Chemical Company, Evonik Industries AG, ExxonMobil Corporation, Ineos Group, LG Chem Ltd., Nan Ya Plastics Corporation, UPC Group, etc. are few key manufacturers in global Plasticizers market.
Chapter 1 Preface 1.1 Report Description 1.1.1 Study Purpose 1.1.2 Target Audience 1.1.3 USP and Key Offerings 1.2 Research Scope 1.3 Research Methodology 1.3.1 Phase I – Secondary Research 1.3.2 Phase II – Primary Research 1.3.3 Phase III – Expert Panel Review 1.3.4 Assumptions
Chapter 2 Executive Summary 2.1 Overview 2.2 Market Snapshot: Plasticizers 2.3 Global Plasticizers Market, by Product Type, 2016 (Tons) (US$ Mn) 2.4 Global Plasticizers Market, by Application, 2016 (Tons) (US$ Mn) 2.5 Global Plasticizers Market Share, by Geography, 2016 (Tons) (US$ Mn)
In today’s data networks, traffic analysis — determining which links are getting congested and why — is usually done by computers at the network’s edge, which try to infer the state of the network from the times at which different data packets reach their destinations.
If the routers inside the network could instead report on their own circumstances, network analysis would be much more precise and efficient, enabling network operators to more rapidly address problems. To that end, router manufacturers have begun equipping their routers with counters that can report on the number of data packets a router has processed in a given time interval.
But raw number counts are only so useful, and giving routers a special-purpose monitoring circuit for every new measurement an operator might want to make isn’t practical. The alternative is for routers to ship data packets to outside servers for more complex analysis, but that technique doesn’t scale well. A data center with 100,000 servers, for instance, might need another 40,000 to 50,000 servers just to keep up with the flood of router data.
Researchers at MIT, Cisco Systems, and Barefoot Networks have come up with a new approach to network monitoring that provides great flexibility in data collection while keeping both the circuit complexity of the router and the number of external analytic servers low. They describe the work in a paper they’re presenting this week at the annual conference of the Association for Computing Machinery’s Special Interest Group on Data Communication.
Dubbed Marple, the system consists of a programming language that enables network operators to specify a wide range of network-monitoring tasks and a small set of simple circuit elements that can execute any task specified in the language. Simulations using actual data center traffic statistics suggest that, in the data center setting, Marple should require only one traffic analysis server for every 40 or 50 application servers.
“There’s this big movement toward making routers programmable and making the hardware itself programmable,” says Mohammad Alizadeh, the TIBCO Career Development Assistant Professor of Electrical Engineering and Computer Science at MIT and a senior author on the paper. “So we were really motivated to think about what this would mean for network-performance monitoring and measurement. What would I want to be able to program into the router to make the task of the network operator easier?
“We realized that it’s going to be very difficult to try to figure this out by picking out some measurement primitives or algorithms that we know of and saying, here’s a module that will allow you to do this, here’s a module that will allow you to do that. It would be difficult to get something that’s future-proof and general using that approach.”
Instead, Alizadeh and his collaborators co-designed the Marple language and the circuitry required to implement Marple queries, with one eye on the expressive flexibility of the language and another on the complexity of the circuits required to realize that flexibility. The team included first author Srinivas Narayana, a postdoc at MIT’s Computer Science and Artificial Intelligence Laboratory; Anirudh Sivaraman, Vikram Nathan, and Prateesh Goyal, all MIT graduate students in electrical engineering and computer science; Venkat Arun, an undergraduate at the Indian Institute of Technology Guwahati who visited MIT for a summer; Vimalkumar Jeyakumar of Cisco Tetration Analytics; and Changhoon Kim of Barefoot Networks.
The idea behind Marple is to do as much analysis on the router itself as possible without causing network delays, and then to send the external server summary statistics rather than raw packet data, incurring huge savings in both bandwidth and processing time.
Marple is designed to individually monitor the transmissions of every computer sending data through a router, a number that can easily top 1 million. The problem is that a typical router has enough memory to store statistics on only 64,000 connections or so.
Marple solves this problem through a variation on the common computer science technique of caching, in which frequently used data is stored close to a processing unit for efficient access. Each router has a cache in which it maintains statistics on the data packets it’s seen from some fixed number of senders — say, 64,000. If its cache is full, and it receives a packet from yet another sender — the 64,001st — it simply kicks out the data associated with one of the previous 64,000 senders, shipping it off to a support server for storage. If it later receives another packet from the sender it booted, it starts a new cache entry for that sender.
This approach works only if newly booted data can be merged with the data already stored on the server. In the case of packet counting, this is simple enough. If the server records that a given router saw 1,000 packets from sender A, and if the router has seen another 100 packets from sender A since it last emptied A’s cache, then at the next update the server simply adds the new 100 packets to the 1,000 it’s already recorded.
But the merge process is not so straightforward if the statistic of interest is a weighted average of the number of packets processed per minute or the rate at which packets have been dropped by the network. The researchers’ paper, however, includes a theoretical analysis showing that merging is always possible for statistics that are “linear in state.”
“Linear” means that any update to the statistic involves multiplying its current value by one number and then adding another number to that product. The “in state” part means that the multiplier and the addend can be the results of mathematical operations performed on some number of previous packet measurements.
“We found that for operations where it wasn’t immediately clear how they’d be written in this form, there was always a way to rewrite them into this form,” Narayana says. “So it turns out to be a fairly useful class of operations, practically.”
“While much work has been done on low-level programmable primitives for measuring performance, these features are impotent without an easier network programming environment so that operators can ask network-level queries without writing low-level queries on multiple routers,” says George Varghese, Chancellor’s Professor of Computer Science at the University of California at Los Angeles. “This paper represents an important step toward a programming-language approach to networks, starting with a network programming abstraction. This is in stark contrast to the state of the art today, which is individual router programming, which is fault prone and gives little visibility into the network as a whole. Further, the network programming language is intuitive, using familiar functional-language primitives, reducing the learning curve for operators.”
The new work was supported by the National Science Foundation, the U.S. Defense Advanced Projects Agency, and Cisco Systems.
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Global Retractable Needle Safety Syringes Market Is Expected To Reach US$ 2,345.7 Mn By 2025
According to the latest market report published by Credence Research, Inc. “Retractable Needle Safety Syringes Market – Growth, Future Prospects, and Competitive Analysis, 2017 – 2025,” the global retractable needle safety syringes market was valued at US$ 1,345 Mn in 2016, and is expected to reach US$ 2,345.7 Mn by 2025, expanding at a CAGR of 6.1% from 2017 to 2025
According to WHO studies, almost 16 billion injections are administered every year; out of which a majority i.e. 90% is given in curative care. General trend of unsafe syringe practice is observed all over the world, especially in developing countries, which has led to transmission of infection amongst the patient and healthcare professional. Though the exact data is not available for disease burden associated with unsafe and reuse of syringe practices, but it would lead to disease such as haemorrhagic fevers such as Ebola and Marburg viruses, bacterial infection, malaria, and others. The WHO has set guidelines for safe practice of syringe usage and developed a global campaign to promote injection safety. These safety guidelines would help to protect health workers against needle stick accidents and further exposure to infection. The guidelines have also recommended the usage of new “smart” syringes that would prevent reuse, urging countries to transition by 2020. Introduction of cost effective safety engineered syringes would drive the growth of the retractable needle safety syringes.
The global retractable needle safety syringes market is segmented by product type into manual retractable syringes and automatic-retractable safety syringes. Manual retractable syringes dominate the retractable needle safety syringes, due to their wide usage and cost effectiveness. The market is also segmented by end users into hospitals, clinics and ambulatory surgery centres. Hospitals dominate the global retractable needle safety syringes market and is rapidly growing with WHO guideline for usage of “smart syringes”
As of the current market scenario, North America dominated the global retractable needle safety syringes market followed by Europe. Factors contributing to the growth of North America includes government initiative and regulation for safety practices, rise in incidence of needlestick injury and enhanced safety needles. Asia Pacific is the fastest growing retractable needle safety syringes market in forecast period.
Market Competition Assessment:
Key players in the global retractable needle safety syringes market are Axel Bio Corporation, Becton, Dickinson and Company, DMC Medical Limited, Globe Medical Tech, Inc., Medtronic Plc, Medigard Limited, Retractable Technologies, Inc., Smiths Medical, Sol-Millennium, UltiMed, Inc. and others.
Just how do individuals designate a reason to occasions they witness? Some thinkers have actually recommended that individuals establish duty for a specific result by visualizing just what would certainly have occurred if a believed reason had actually not interfered.
This sort of thinking, called counterfactual simulation, is thought to take place in several scenarios. Football umpires determining whether a gamer must be attributed with an “very own objective”– an objective unintentionally racked up for the opposing group– have to attempt to establish just what would certainly have taken place had the gamer not touched the sphere.
This procedure could be aware, as in the football instance, or subconscious, to make sure that we are not also conscious we are doing it. Utilizing innovation that tracks eye motions, cognitive researchers at MIT have actually currently gotten the initial straight proof that individuals subconsciously make use of counterfactual simulation to think of exactly how a scenario might have played out in a different way.
” This is the very first time that we or anyone have actually had the ability to see those simulations occurring online, to count the number of an individual is making, as well as reveal the connection in between those simulations as well as their judgments,” claims Josh Tenenbaum, a teacher in MIT’s Department of Brain and also Cognitive Sciences, a participant of MIT’s Computer Science as well as Artificial Intelligence Laboratory, as well as the elderly writer of the brand-new research.
Tobias Gerstenberg, a postdoc at MIT that will certainly be signing up with Stanford’s Psychology Department as an assistant teacher following year, is the lead writer of the paper, which shows up in the Oct. 17 problem of Psychological Science. Various other writers of the paper are MIT postdoc Matthew Peterson, Stanford University Associate Professor Noah Goodman, as well as University College London Professor David Lagnado.
Adhere to the round
Previously, researches of counterfactual simulation might just utilize records from individuals explaining just how they made judgments concerning duty, which supplied just indirect proof of exactly how their minds were functioning.
Gerstenberg, Tenenbaum, and also their coworkers laid out to discover even more straight proof by tracking individuals’s eye motions as they viewed 2 billiard spheres clash. The scientists developed 18 video clips revealing various feasible end results of the accidents. In many cases, the crash knocked among the rounds via a gateway; in others, it stopped the sphere from doing so.
Prior to viewing the video clips, some individuals were informed that they would certainly be asked to price exactly how highly they concurred with declarations associated with round A’s impact on round B, such as, “Ball A triggered round B to undergo eviction.” Various other individuals were asked merely just what the result of the crash was..
As the topics saw the video clips, the scientists had the ability to track their eye motions utilizing an infrared light that shows off the student as well as exposes where the eye is looking. This enabled the scientists, for the very first time, to obtain a home window right into exactly how the mind pictures feasible results that did not take place.
” What’s actually amazing concerning eye monitoring is it allows you see points that you’re not purposely knowledgeable about,” Tenenbaum states. “When theorists as well as psycho therapists have actually suggested the suggestion of counterfactual simulation, they have not always suggested that you do this knowingly. It’s something taking place behind the surface area, and also eye monitoring has the ability to disclose that.”.
The scientists discovered that when individuals were asked concerns concerning round A’s result on the course of sphere B, their eyes adhered to the training course that round B would certainly have taken had round A not conflicted. The a lot more unpredictability there was as to whether sphere A had an impact on the end result, the much more typically individuals looked towards round B’s fictional trajectory.
” It’s in the close instances where you see one of the most counterfactual appearances. They’re making use of those seeks to fix the unpredictability,” Tenenbaum claims.
Individuals that were asked just what the real result had actually been did not execute the very same eye activities along round B’s option path.
Individuals asked this inquiry primarily looked at the rounds and also attempted to anticipate where sphere B would certainly go. The individual on the right was asked to evaluate whether sphere A triggered round B to go via the entrance. Individuals asked this inquiry attempted to replicate where round B would certainly have gone if sphere A had not been existing in the scene.
The scientists are currently utilizing this strategy to research a lot more complicated scenarios where individuals make use of counterfactual simulation to earn judgments of origin.
” We assume this procedure of counterfactual simulation is actually prevalent,” Gerstenberg claims. “In lots of instances it could not be sustained by eye motions, due to the fact that there are several sort of abstract counterfactual reasoning that we simply carry out in our mind. The billiard-ball crashes lead to a specific kind of counterfactual simulation where we could see it.”.
One instance the scientists are researching is the following: Imagine round C is goinged for the entrance, while spheres An as well as B each head towards C. Either one can knock C off program, yet An obtains there. Is B off the hook, or should it still birth some obligation for the result?
” Part of just what we are attempting to do with this job is obtain a little bit a lot more quality on just how individuals deal with these intricate situations. We’re all in the exact same video game of attempting to comprehend exactly how individuals believe concerning causation.”.
The study was moneyed by the National Science Foundation with MIT’s Center for Machines, minds as well as minds, as well as by the Office of Naval Research.
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In some instances, the accident knocked one of the rounds via an entrance; in others, it avoided the sphere from doing so.
The individual on the left was asked to evaluate whether they believed that round B went with the center of the gateway. Individuals asked this inquiry mainly looked at the rounds as well as attempted to anticipate where sphere B would certainly go. The individual on the right was asked to evaluate whether round A triggered round B to go via the gateway. Individuals asked this inquiry attempted to imitate where round B would certainly have gone if round A had not been existing in the scene.
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