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WELCOME TO MY TECHNICAL UNIVERSE the physics of cognitive systems

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.)

  1. 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.
  2. 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.

Satellites Rock! 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?Cosmic matter budget 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

.Galaxy cluster 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.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: Supernovae 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..

.Lyman Alpha Forest 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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Life 3.0 Being Human in the Age of Artificial Intelligence

Cover of book called Life 3.0

Max Tegmark

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Max Tegmark
Max Tegmark.jpg
Born(1967-05-05) May 5, 1967 (age 50)
Alma materRoyal Institute of Technology
UC Berkeley
Scientific career
FieldsCosmology, Physics

Max Erik Tegmark[1] (born Max Shapiro[2][3] 5 May 1967) is a Swedish-American cosmologist. Tegmark is a professor at the Massachusetts Institute of Technology and the scientific director of the Foundational Questions Institute. He is also a co-founder of the Future of Life Institute, and has accepted donations from Elon Musk to investigate existential risk from advanced artificial intelligence.[4][5][6]


Early life[edit]

Tegmark was born in Sweden, the son of Karin Tegmark and American-born professor emeritus of mathematics Harold S. Shapiro. He graduated from the Royal Institute of Technology in Stockholm, Sweden and the Stockholm School of Economics and later received his PhD from the University of California, Berkeley. After having worked at the University of Pennsylvania, he is now at the Massachusetts Institute of Technology. While in high school, Tegmark and a friend created and sold a word processor written in pure machine code for the Swedish eight-bit computer ABC 80,[2] and a 3D Tetris-like game.[7]


His research has focused on cosmology, combining theoretical work with new measurements to place constraints on cosmological models and their free parameters, often in collaboration with experimentalists. He has over 200 publications, of which nine have been cited over 500 times.[8] He has developed data analysis tools based on information theory and applied them to cosmic microwave background experiments such as COBE, QMAP, and WMAP, and to galaxy redshift surveys such as the Las Campanas Redshift Survey, the 2dF Survey and the Sloan Digital Sky Survey.

With Daniel Eisenstein and Wayne Hu, he introduced the idea of using baryon acoustic oscillations as a standard ruler.[9][10] With Angelica de Oliveira-Costa and Andrew Hamilton, he discovered the anomalous multipole alignment in the WMAP data sometimes referred to as the “axis of evil”.[9][11] With Anthony Aguirre, he developed the cosmological interpretation of quantum mechanics.

Tegmark has also formulated the “Ultimate Ensemble theory of everything“, whose only postulate is that “all structures that exist mathematically exist also physically”. This simple theory, with no free parameters at all, suggests that in those structures complex enough to contain self-aware substructures (SASs), these SASs will subjectively perceive themselves as existing in a physically “real” world. This idea is formalized as the mathematical universe hypothesis,[12] described in his book Our Mathematical Universe.

Tegmark was elected Fellow of the American Physical Society in 2012 for, according to the citation, “his contributions to cosmology, including precision measurements from cosmic microwave background and galaxy clustering data, tests of inflation and gravitation theories, and the development of a new technology for low-frequency radio interferometry”.[13]

Personal life[edit]

He was married to astrophysicist Angelica de Oliveira-Costa in 1997, and divorced in 2009. They have two sons.[14] On August 5, 2012, Tegmark married Meia Chita, a Boston University Ph.D. candidate.[15][16]

In the media[edit]



  1. Jump up ^ Max Tegmark Faculty page, MIT Physics Department
  2. ^ Jump up to: a b “buzzword free zone – home of magnus bodin”. Retrieved 2012-11-01. 
  3. Jump up ^ Sveriges befolkning 1980, CD-ROM, Version 1.02, Sveriges Släktforskarförbund (2004).
  4. Jump up ^ The Future of Computers is the Mind of a Toddler, Bloomberg
  5. Jump up ^ “Elon Musk:Future of Life Institute Artificial Intelligence Research Could be Crucial”. Bostinno. 2015. Retrieved 21 Jun 2015. 
  6. Jump up ^ “Elon Musk Donates $10M To Make Sure AI Doesn’t Go The Way Of Skynet”. TechCrunch. 2015. Retrieved 21 Jun 2015. 
  7. Jump up ^ Tegmark, Max. The Mathematical Universe. p. 55. 
  8. Jump up ^ “INSPIRE-HEP: M Tegmark’s profile”. Inspire-Hep. 
  9. ^ Jump up to: a b “Tegmark – Philosophy of Cosmology”. Retrieved 2016-02-15. 
  10. Jump up ^ Eisenstein, Daniel J.; Hu, Wayne; Tegmark, Max. “Cosmic Complementarity: H 0 {\displaystyle H_{0}} and Ω m {\displaystyle \Omega _{m}} from Combining Cosmic Microwave Background Experiments and Redshift Surveys”. The Astrophysical Journal. 504 (2): L57–L60. Bibcode:1998ApJ…504L..57E. arXiv:astro-ph/9805239Freely accessible. doi:10.1086/311582. 
  11. Jump up ^ Tegmark, Max; de Oliveira-Costa, Angélica; Hamilton, Andrew (1 December 2003). “High resolution foreground cleaned CMB map from WMAP”. Physical Review D. 68 (12). Bibcode:2003PhRvD..68l3523T. arXiv:astro-ph/0302496Freely accessible. doi:10.1103/PhysRevD.68.123523. 
  12. Jump up ^ Tegmark, Max. “The Mathematical Universe”. Foundations of Physics. 38 (2): 101–150. Bibcode:2008FoPh…38..101T. arXiv:0704.0646Freely accessible. doi:10.1007/s10701-007-9186-9.  a short version of which is available at Shut up and calculate. (in reference to David Mermin’s famous quote “shut up and calculate” [1]
  13. Jump up ^ APS Archive (1990-present)
  14. Jump up ^ “Max Tegmark Homepage”. Retrieved 2012-11-01. 
  15. Jump up ^ “Welcome to Meia and Max’s wedding”. Retrieved 2014-01-10. 
  16. Jump up ^ “Meia Chita-Tegmark”. Huffington Post. Retrieved 2015-01-10. 
  17. Jump up ^ “Max Tegmark forecasts the future”. New Scientist. 18 November 2006. Retrieved 2012-11-01. 
  18. Jump up ^ The Forum episode guide. BBC Radio 4. Accessed 2014-04-28.
  19. Jump up ^ The Perpetual Earth Program
  20. Jump up ^
  21. Jump up ^ “The Multiverse & You (& You & You & You…)”. Sam Harris. 23 September 2015. Retrieved 2015-11-22. 
  22. Jump up ^ “The Future of Intelligence)”. Sam Harris. 27 A

Artificial intelligence

Artificial intelligence

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.[1] 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”.[2]

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.”[3] For instance, optical character recognition is frequently excluded from “artificial intelligence”, having become a routine technology.[4] Capabilities generally classified as AI, as of 2017, include successfully understanding human speech,[5] competing at a high level in strategic game systems (such as chess and Go[6]), 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,[7][8] followed by disappointment and the loss of funding (known as an “AI winter”),[9][10] followed by new approaches, success and renewed funding.[11] For most of its history, AI research has been divided into subfields that often fail to communicate with each other.[12] 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.[11]

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.[13] General intelligence is among the field’s long-term goals.[14] 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”.[15] 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.[16] Some people also consider AI a danger to humanity if it progresses unabatedly.[17]

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.[18]

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Clear And Objective Facts Concerning Just what Is A Drone? (Without All the Buzz).

A drone, additionally referred to as an unmanned aerial vehicle (UAV) as well as many various other names, is a tool that will fly without the use of a pilot or any individual on board. These ‘aircraft’ could be controlled remotely utilizing a push-button control tool by someone standing on the ground or by utilizing computers that are on-board. UAV’s at first were normally regulated by a person on the ground yet as technology has progressed, more and more airplane are being made with the purpose of being managed using on-board computers.

The suggestion of an unmanned aerial lorry could be traced back to early in the twentieth century as well as were initially planned to be solely used for military objectives yet have actually considering that found location in our daily lives. Reginald Denny, that was a popular film celebrity in addition to an enthusiastic enthusiast of design planes was stated to produce the first ever remote piloted automobile in 1935. Given that this day, the airplane have actually had the ability to adjust to brand-new modern technologies and could currently be discovered with video cameras along with other beneficial extras. As an outcome of this, UAVs are utilized for policing, safety and security job as well as security as well as firefighting, they are even used by many companies to look at difficult to get to assets such as piping and wirework adding an added layer of safety and safety and security.

The surge in popularity of these tools has nevertheless, brought some downsides as well as positives as new regulations and also guidelines have needed to be presented to manage the situation. As the UAVs were obtaining stronger as well as modern technologies were improving, it indicated that they could fly greater as well as even more far from the driver. This has brought about some problems with flight terminal disturbance all over the globe. In 2014, South Africa announced that they needed to tighten up safety when it involves prohibited flying in South African airspace. A year later and the US introduced that they were holding a conference to talk about the demands of signing up a commercial drone.

In addition to the formerly pointed out usages, drones are currently also used for checking of plants, counting animals in a certain location, evaluating a group amongst numerous others. Drones have handled to change the manner in which many sectors are run and also have also enabled many organisations to become extra reliable. Drones have actually likewise assisted to increase safety as well as contribute when it concerns saving lives. Woodland fires and also all-natural catastrophes could be kept an eye on and also the drone could be made use of to alert the appropriate authorities of anyone that is in trouble and also looking for aid. The specific location of these events could also be discovered with ease.

Drones have likewise come to be a pastime for many people around the world. In the United States, leisure use such a gadget is legal; however, the owner needs to take some precautions when attempting to fly. The airplane should stick to certain guidelines that have actually been outlined; as an example, the tool could not be more than 55 extra pounds. The drone ought to also prevent being utilized in a manner that will hinder airport terminal operations and also if a drone is flown within five miles of an airport, the airports traffic control tower need to be informeded in advance.

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Currently You Can Have Your cognitive computing, or expert system (AI), modern technologies Done Safely

Inning accordance with new IBM study, released Thursday, more than 73% of Chief executive officers anticipate that cognitive computing, or expert system (AI), innovations will play a “crucial duty” in their organisation’s future. The press release revealing the findings additionally noted that more than 50% of these CEOs intend to take on these technologies by the year 2019.

In regards to where AI will certainly verify most impactful, participants detailed infotech, sales, and information safety and security as the top 3 concerns for the modern technology. The 6,000 Chief executive officers surveyed also stated they anticipate a 15% return on their AI financial investments.

In regards to IT, cognitive remedies can boost software program growth and testing, lead to better efficiency and dexterity, and enhance solution design the launch said. Sales professionals get access to far better information for lead management and could be more reliable in their account administration. Cognitive computing might also speed risk and fraud discovery and free up workers, the release stated.

Much more regarding Development

Exactly how the College of Kentucky made use of technology to change classroom learning for college students.

IBM is understood for its cognitive computing remedy, Watson, which famously won the TELEVISION video game reveal Risk. While IBM has actually distanced itself from the term “AI” in its use “cognitive computer,” the technologies complete many of the very same goals.

As part of the research study, IBM additionally offered certain recommendations on how business can use AI or cognitive computer to power their electronic transformation efforts. For starters, preparation is important. IBM advises laying out an 18-to-24-month approach for embracing these innovations.

” Define business or service system reinvention instance, KPIs, as well as targets. Apply a targeted operating version and also administration that support this method,” journalism launch claimed.

Successive is the ideation stage. Occasionally, services should examine the marketplace and just how specific individuals are reacting. Try out these technologies to establish best techniques and also typical use instances, the report stated. Make sure to tailor your requirements to your personal company.

” Layout and also carry out pilots with dexterity and also restricted danger to existing consumers and operations. When these concepts are nurtured, advertised, as well as scaled, utilize a lean administration design to occasionally review progress and also worth. Monitor business instance worth realization and also make adjustments, as necessary,” the launch said.

The 3 large takeaways.

A new IBM study record asserts that 73% of CEOs forecast cognitive computer will play a “vital function” in the future of their business.

The report also said that greater than 50% of CEOs plan to adopt the modern technology by 2019, and also they expect a 15% return on their financial investment.

IBM advises describing a prepare for fostering of these innovations, prior to ideating, breeding, and also scaling your remedies.

Why Your Start-up Needs a Social network Community to Grow your organisation

The very early days of a startup are repetitive. Developing an item needs much screening and insight from your individual base. To navigate these very early days effectively, you’ll need data. Consequently, social media neighborhoods are essential for startups. An energetic, involved neighborhood is ideal for getting comments as well as learning their requirements. With a social media sites community, you acquire information that makes your item better.

Areas give users a feeling of company with the start-up. They also push a startup’s growth. This, then, gives startups a competitive advantage.


A Startup Social network Neighborhood Urges Evangelism

By taking advantage of a passionate group, you develop a society of brand evangelists and ambassadors. These users drum up buzz as well as excitement for your brand throughout the net. With a community of evangelists, your startup becomes a full-fledged motion.

Social Media Communities Welcome Novices

Startups might utilize their areas as a support network to troubleshoot for new individuals. This offsets the troubleshooting load for a little startup group. As an incentive, specifically valuable users will certainly really feel more connected to the brand name. This group may additionally offer necessary reviews and also endorsements.

A Solid Social media site Neighborhood = Competitive Advantage

Let’s state your start-up is making waves, as well as competition arises. It’s easy to copy product features. On the various other hand, reproducing community is an entire various story. If you’ve developed a devoted social networks startup area, its users will certainly stick up for your firm. This gives you an advantage over the competition.


Select the Right Social Platform

Since nobody social network is king, you must create a multi-channel technique. That claimed, it’s finest to choose 2 or 3 systems to focus on when starting out.

This doesn’t have to be a tough choice. A style brand, as an example, would certainly discover an all-natural fit on visually concentrated Instagram. Grow your base upon your 2 or 3 platforms of selection. When you have actually grasped those, extend to more.

Usage Your Social media site Community to Instruct

Setting up a how-to network aids beginners feel welcome. It additionally supplies a location for your team to share suggestions with one another. You could begin by composing tutorials to help customers comprehend your product. Welcome others to share their stories and how-tos. A joint team encourages your clients.

If you desire an instance of such a community, take a look at the Stone subreddit on Reddit. Here, customers as well as Pebble workers aid each other out with the firm’s products. They remain to do so long after the business was obtained by Fitbit.

Start Conversation in a Social Area by Promoting It

Ideally, you want a team that sustains itself with a base of customers generating useful as well as appealing web content for each other. First, you should get the sphere rolling by posting material of your personal. Begin by focusing on the start-up’s core worths. Once you have actually demonstrated how your brand name sees things, motivate others to jump in!

Do not Shy Away from Problem

Remember the old Mac vs COMPUTER argument (or, much more just recently, the Android vs iOS one)? A little friendly competition does a great deal to stimulate community growth. Permit your advocates to express their love for your brand authentically. So long as conversation fits within core startup values, the area may be encouraged by competitors and push development.

Maintain Your Followers Safe

Most of us want a prospering social media startup community, however it comes at a cost. A lot of task suggests a lot of conversations to modest. It could be challenging for new businesses to regulate a startup community on social networks. Fortunately, with the right tools, you can keep your followers risk-free much more quickly.

Smart Moderation is a tool that automatically discovers and also erases bothersome language within the minute it’s posted. With sophisticated artificial intelligence, your group could make certain a risk-free, welcoming group with no additional work. The device functions throughout multiple systems and also calls for no coding understanding or experience. Have a look at what sets it aside from other tools below, and learn just how you can develop an awesome group for a startup!

Social MediaSocial media communitiesSocial media neighborhood social media startup communityStartup area


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Next Generation Sequencing Market:

According to the latest report published by Credence Research, Inc. “Next Generation Sequencing Market – (Technology Type – Whole Genome Sequencing, Targeted Resequencing, RNA Sequencing, Whole Exome Sequencing, and De Novo Sequencing); (Application – Oncology, Genetic Screening, Infectious Diseases, Drug and Biomarker Discovery, Agriculture & Animal Research, Idiopathic Diseases and others): Market Growth, Future Prospects and Competitive Analysis, 2016-2024” the market was valued at USD 2.6 Bn in 2015, and is expected to reach USD 20.6 Bn by 2025, expanding at a CAGR of 21.5% from 2016 to 2025.

Browse the full report Next Generation Sequencing Market – Growth, Future Prospects and Competitive Analysis, 2016-2025 report at

Market Insights

Next generation sequencing is a high-throughput sequencing that enables sequencing and assembling of number of short DNA reads at a small period of time and with a better accuracy. The introduction of next generation sequencing technologies has ensured massive changes in the sequencing process by providing better output, higher speed, flexibility and reduced sequencing cost over thousand folds. Technologically, the approaches for any next generation sequencing procedure can be categorized into whole genome sequencing, targeted resequencing, Rna sequencing, whole exome sequencing and de novo sequencing. With the advancement in technology and development of high throughput sequencing platforms such as HiSeq and MiSeq, it has become increasingly efficient to sequence larger number of base pairs in single cycle reads. Targeted resquencing held the largest share in the global next generation sequencing market due to its accuracy, and its rising preferences in the research and development. De novo sequencing is anticipated to grow at the fastest rate during the forecast period due to its faster, more accurate characterization of any species compared to traditional methods.

Next-generation sequencing (NGS) technologies have progressive advantages in terms of cost-effectiveness, unprecedented sequencing speed, high resolution and accuracy in genomic analyses. Technological developments in the next generation sequencing market are expected to enable researchers to generate phase resolved HLA sequences in single read cycles and provide insight into the lesser accessible regions of HLA genes. Furthermore, the development of prenatal genome sequencing for analysis of genetic anomalies and diseases is also expected to witness enhanced demand, thus driving the genetic screening market. Growing incidences of cancer and infectious diseases, and increasing use of next generation sequencing to develop biopharmaceuticals and drugs for their cure are further expected to drive demand for next generation sequencing.

Geographically, North America was observed as the largest revenue generating market for next generation sequencing market, where the U.S. held the largest market share. The major factors driving the market are technological advancements in the field of life sciences, availability of commercial solutions for next generation sequencing data analysis, and presence of key players in the region. Moreover, extended support from the government institution for genomic research for drug discovery and genetic screening is also driving the market for next generation sequencing in North America and Europe. Asia-Pacific is expected to grow at the fastest rate with throughout the forecast period owing to growing medical awareness in the regional population, increasing investment for development of healthcare and rising oncology and infectious disease research in the region.

Market Competition Assessment:

The next generation sequencing market currently possesses numerous companies having their products marketed, however, Illumina, Inc. dominates the market wholly. Most of the companies are located in the North America and others developed regions. The companies have untapped opportunities in the developing regions of Asia Pacific and Latin America. Companies are coming up with various products in the developed nations due to high acceptance and accessibilities of these products. The companies include, ThermoFisher Scientific, Pacific Biosciences of California, F.Hoffman-La Roche AG Qiagen, BGI and Others.

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Key Market Movements:

  • Continuous introduction of new products and high level of accuracy offered by the products have accelerated the demand of next generation sequencers
  • Extended clinical application of the next generation sequencing technologies in the field of various diseases and drug discovery driving the market

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Our customers mirror our worldwide nature. Around 45% are in Europe, 30% in the Americas, 13% in Asia Pacific and 12% in the Middle East and Africa.

Our firm is intended to work as one. We are a solitary global research organization united by a solid arrangement of qualities, concentrated on customer effect.


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2How To Get (A) Fabulous Artificial intelligence On A Tight Budget

Identifying optimal product prices

How can online businesses leverage vast historical data, computational power, and sophisticated machine-learning techniques to quickly analyze and forecast demand, and to optimize pricing and increase revenue?

A research highlight article in the Fall 2017 issue of MIT Sloan Management Review by MIT Professor David Simchi-Levi describes new insights into demand forecasting and price optimization.

Algorithm increases revenue by 10 percent in six months

Simchi-Levi developed a machine-learning algorithm, which won the INFORMS Revenue Management and Pricing Section Practice Award, and first implemented it at online retailer Rue La La.

The initial research goal was to reduce inventory, but what the company ended up with was “a cutting-edge, demand-shaping application that has a tremendous impact on the retailer’s bottom line,” Simchi-Levi says.

Rue La La’s big challenge was pricing on items that have never been sold before and therefore required a pricing algorithm that could set higher prices for some first-time items and lower prices for others.

Within six months of implementing the algorithm, it increased Rue La La’s revenue by 10 percent.

Forecast, learn, optimize

Simchi-Levi’s process involves three steps for generating better price predictions:

The first step involves matching products with similar characteristics to the products to be optimized. A relationship between demand and price is then predicted with the help of a machine-learning algorithm.

The second step requires testing a price against actual sales, and adjusting the product’s pricing curve to match real-life results.  

In the third and final step, a new curve is applied to help optimize pricing across many products and time periods.

Predicting consumer demand at Groupon

Groupon has a huge product portfolio and launches thousands of new deals every day, offering them for only a short time period. Since Groupon has such a short sales period, predicting demand was a big problem and forecasting near impossible.

Applying Simchi-Levi’s approach to this use case began by generating multiple demand functions. By then applying a test price and observing customers’ decisions, insights were gleaned on how much was sold — information that could identify the demand function closest to the level of sales at the learning price. This was the final demand-price function used, and it was used as the basis for optimizing price during the optimization period.

Analysis of the results from the field experiment showed that this new approach increased Groupon’s revenue by about 21 percent but had a much bigger impact on low-volume deals. For deals with fewer bookings per day than the median, the average increase in revenue was 116 percent, while revenue increased only 14 percent for deals with more bookings per day than the median.

Potential to disrupt consumer banking and insurance

The ability to automate pricing enables companies to optimize pricing for more products than most organizations currently find possible. This method has also been used for a bricks-and-mortar application by applying the method to a company’s promotion and pricing, in various retail channels, with similar results.

“I am very pleased that our pricing algorithm can achieve such positive results in a short timeframe,” Simchi-Levi says. “We expect that this method will soon be used not only in retail but also in the consumer banking industry. Indeed, my team at MIT has developed related methods that have recently been applied in the airline and insurance industries.”


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Roofing Underlayment Market Regulations, Size, Share and Competitive Landscape Outlook to 2025

The latest market report published by Credence Research, Inc. “Roofing Underlayment Market – Growth, Future Prospects and Competitive Analysis, 2017 – 2025,” the global roofing underlayment market was valued at US$ 27,478.6 Mn in 2016, and is expected to each US$ 44,490.5 Mn by 2025, expanding at a CAGR of 5.5% from 2017 to 2025.

Market Insights

The global roofing underlayment market has witnessed a significant rise in strategic mergers and collaborations among roofing underlayment manufacturers. Such growth strategies are focused at augmenting their service portfolio. The demand for a variety of roofing underlying materials has started gaining strength. However, the market is on the brisk of a huge alteration as emerging economies such as Asia Pacific and Latin America are imposing most influential positions in the market. The booming construction sector and a constant rise in new construction activities in these emerging economies are mostly to be ascribed for the excellent growth prospects for roofing underlying materials in the regions. In North America and Europe, on the other hand, demand is mostly stimulated by refurbishing and maintenance activities.

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Browse the full Roofing Underlayment Market – Growth, Future Prospects and Competitive Analysis, 2017 – 2025 report at

The non-residential sector is presently the largest contributor to the global roofing underlying materials market, accounting for a share of nearly 44% in the global market. The segment growth is expected to slow down to the residential sector with a slight decline in its overall share in the global market in 2025. Demand for a variety of roofing underlying materials in the commercial sector will be driven largely by continuous rise in replacement and maintenance activities post the recent economic downturn. Stabilizing economies are enabling the commercial sector to take up previously deferred replacement projects.

Asia Pacific held a dominant share of nearly 41% in the global roofing underlying materials market in 2016, mainly owing to the booming construction industry and constantly rising numbers of new homeowners in urban parts of the region. The market for roofing underlying materials is also expected to expand at the fastest pace in Asia Pacific as compared to other regional markets. In Latin America and the Middle East and Africa, the markets for roofing underlying materials will show strong growth and will benefit the most from developments in the residential construction sector.

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How Expert system Advertising and marketing is Changing the Game

Expert system advertising and marketing takes points one step even more compared to Search Engine Optimization techniques and so on: with artificial intelligence, AI can learn and also alter formulas to work extra effectively. This means that as you use an AI-powered application, it becomes better at its task. As well as thanks to natural language handling, customers can interact with expert system based devices equally as they would certainly with a human.

Using Artificial Intelligence to Build Actual Relationships

Retention Scientific research (RS) is a B-to-B Expert system advertising and marketing technology that helps merchants as well as brand names recognize, involve as well as retain their consumers. It accurately anticipates customer actions and also utilizes those insights to carry out one-to-one e-mail, web site as well as mobile advertising campaigns at scale to boost conversion prices and profits. Founded in 2013 and locateded in L.a, Retention Science powers campaigns for Target, Dollar Shave Club, The Honest Firm, BCBG, Wet Seal, and also numerous other ingenious ecommerce brand names.

Customer Marketing 2017

We are going into a new age of marketing– the period of Artificial Intelligence Advertising (OBJECTIVE)– an age where makers run 1,000’s of recursive tests and take care of the mathematical optimization of consumer worth development, but the marketer remains in control while investing more time being strategic as well as creative. This session will discover the structures of GOAL and also analyze real-world usage cases where clients from markets like video gaming, telco, as well as financial have actually realized product development in customer worth metrics including consumer retention as well as ordinary revenue each user (ARPU).

Expert system Advertising (PURPOSE).

Did I claim 100 lessons? I suggested 4. Why 4? It fits. Lesson number 4. Heros do win, under appealing as well as over delivering does work, and also expert system advertising and marketing is real. OK, that is 3 lessons knocked into one sentence, and also among them is a saying, however once more, this is my blog, I reach create just what I desire. I expect getting on phase, getting hold of the mic, and throwing. My fascination with marketing innovation is certainly clear. Over the last decade, I have continued to be just one of one of the most energetic capitalists in the room. This will be fun. Allow the revolution start. All set to welcome tomorrow.

3 Reasons Why Expert system Advertising and marketing is Here to Remain|WGN Radio – 720 AM.

Just what was when viewed as the material of sci-fi flicks, expert system seems a lot more of a reality compared to previously expected. Expert system marketing can play such a significant role in the development of brand analysis as well as consumer interactions. Between sentiment analysis, customer support possibilities, and advertising and marketing optimization, expert system permits marketers to obtain a much better understanding of their consumer base.