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Microchip Technology Inc.

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Microchip Technology Incorporated develops, manufactures, and sells semiconductor products for various embedded control applications in the Americas, Europe, and Asia. The company offers general purpose and specialized 8-bit, 16-bit, and 32-bit microcontrollers; 32-bit microprocessors; and microcontrollers for automotive, industrial, computing, communication, lighting, power supplies, motor control, human machine interface, security, and wired and wireless connectivity applications. It also provides development tools that enable system designers to program microcontroller products for specific applications; field-programmable gate array (FPGA) products; and analog, power, interface, mixed signal, and timing products comprising power management, linear, mixed-signal, high-voltage, thermal management, discrete diodes and metal oxide semiconductor field effect transistors (MOSFETS), radio frequency (RF), drivers, safety, security, timing, USB, Ethernet, wireless, and other interface products. In addition, the company offers memory products consisting of serial electrically erasable programmable read-only memory, serial flash memories, parallel flash memories, serial static random access memories, and serial electrically erasable random access memories for the production of very small footprint devices; and licenses its SuperFlash embedded flash and Smartbits one time programmable NVM technologies to foundries, integrated device manufacturers, and design partners for use in the manufacture of microcontroller products, gate array, RF, analog, and neuromorphic compute products that require embedded non-volatile memory, as well as provides engineering services. Further, it offers wafer foundry and assembly and test subcontracting manufacturing services; and aerospace and timing systems products, application specific integrated circuits, and complex programmable logic devices. Microchip Technology Incorporated was founded in 1989 and is headquartered in Chandler, Arizona.

MICROCHIP TECHNOLOGY INCORPORATED

Is Astronomy data science? Machine learning in Astronomy – sure it sounds like an oxymoron, but is that really the case? Machine learning is one of the newest ‘sciences’, while astronomy – one of the oldest. In fact, Astronomy developed naturally as people realized that studying the stars is not only fascinating, but it can also help them in their everyday life. For example, investigating the star cycles helped creating calendars (such as the Maya and the Proto-Bulgarian calendar). Moreover, it played a crucial role in navigation and orientation. A particularly important early development was the use of mathematical, geometrical and other scientific techniques to analyze the observed data. It started with the Babylonians, who laid the foundations for the astronomical traditions that would be later maintained in many other civilizations. Since then, data analysis has played a central role in astronomy. So, after millennia of refining techniques for data analysis, you would think that no dataset could present a problem to astronomers anymore, right? Well… that’s not entirely true. The main problem that astronomers face now is… as strange as it may sound… the advances in technology. Wait, what?! How can better technology be a problem? It most certainly can. Because what I mean by better technology is a bigger field of view (FOV) of the telescopes and higher resolution of the detectors. Those factors combined indicate that today’s telescopes gather a great deal more data than previous generation tech. And that suggests astronomers must deal with volumes of data they’ve never seen before. How was the Galaxy Zoo Project born? In 2007, Kevin Schawinski found himself in that kind of situation. As an astrophysicist at Oxford University, one of his tasks was to classify 900,000 images of galaxies gathered by the Sloan Digital Sky Survey for a period of 7 years. He had to look at every single image and note whether the galaxy was elliptical or spiral and if it was spinning. The task seems like a pretty trivial one. However, the huge amount of data made it almost impossible. Why? Because, according to estimations, one person had to work 24/7 for 3-5 years in order to complete it! Talking about huge workload! So, after working for a week, Schawinski and his colleague Chris Lintott decided there had to be a better way to do this. That is how Galaxy Zoo – a citizen science project – was born. If you’re hearing it for the first time, citizen science means that the public participates in professional scientific research. Basically, the idea of Schawinski and Lintott was to distribute the images online and recruit volunteers to help out and label the galaxies. And that is possible because the task of identifying the galaxy as elliptic or spiral is pretty straightforward. Initially, they hoped for 20,000 – 30,000 people to contribute. However, much to their surprise, more than 150,000 people volunteered for the project and the images were classified in about 2 years. Galaxy Zoo was a success and more projects followed, such as Galaxy Zoo Supernovae and Galaxy Zoo Hubble. In fact, there are several active projects to this day. Using thousands of volunteers to analyze data may seem like a success but it also shows how much trouble we are in right now. 150,000 people in the space of 2 years managed to just classify (and not even perform complex analysis on) data gathered from just 1 telescope! And now we are building a hundred, even a thousand times more powerful telescopes. That said, in a couple of years’ time volunteers won’t be enough to analyze the huge amounts of data we receive. To quantify this, the rule of thumb in astronomy is that the information we collect is doubling every year. As an example, The Hubble Telescope operating since 1990 gathers around 20GB of data per week. And the Large Synoptic Survey Telescope (LSST), scheduled for early 2020, is expected to gather more the 30 terabytes of data every night. But that is nothing compared to the most ambitious project in astronomy – the Square Kilometre Array (SKA). SKA is an intergovernmental radio telescope to be built in Australia and South Africa with projected completion around 2024. With its 2000 radio dishes and 2 million low-frequency antennas, it is expected to produce more than 1 exabyte per day. That’s more than the entire internet for a whole year, produced in just one day! Wow, can you imagine that!? With that in mind, it is clear that this monstrous amount of data won’t be analyzed by online volunteers. Therefore, researchers are now recruiting a different kind of assistants – machines. Why is everyone talking about Machine Learning? Big data, machines, new knowledge… you know where we’re going, right? Machine learning. Well, it turns out that machine learning in astronomy is a thing, too. Why? First of all, machine learning can process data much faster than other techniques. But it can also analyze that data for us without our instructions on how to do it. This is extremely important, as machine learning can grasp things we don’t even know how to do yet and recognize unexpected patterns. For instance, it may distinguish different types of galaxies before we even know they exist. This brings us to the idea that machine learning is also less biased than us humans, and thus, more reliable in its analysis. For example, we may think there are 3 types of galaxies out there, but to a machine, they may well look like 5 distinct ones. And that will definitely improve our modest understanding of the universe. No matter how intriguing these issues are, the real strength of machine learning is not restricted to just solving classification issues. In fact, it has much broader applications that can extend to problems we have deemed unsolvable before. What is gravitational lensing? In 2017, a research group from Stanford University demonstrated the effectiveness of machine learning algorithms by using a neural network to study images of strong gravitational lensing. Gravitational lensing is an effect where the strong gravitational field around massive objects (e.g. a cluster of galaxies) can bend light and produce distorted images. It is one of the major predictions of Einstein’s General Theory of Relativity. That’s all well and good, but you might be wondering, why is it useful to study this effect? Well, the thing you need to understand is that regular matter is not the only source of gravity. Scientists are proposing that there is “an invisible matter”, also known as dark matter, that constitutes most of the universe. However, we are unable to observe it directly (hence, the name) and gravitational lensing is one way to “sense” its influence and quantify it. Previously, this type of analysis was a tedious process that involved comparing actual images of lenses with a large number of computer simulations of mathematical lensing models. This could take weeks to months for a single lens. Now that’s what I would call an inefficient method. But with the help of neural networks, the researchers were able to do the same analysis in just a few seconds (and, in principle, on a cell phone’s microchip), which they demonstrated using real images from NASA’s Hubble Space Telescope. That’s certainly impressive! Overall, the ability to sift through large amounts of data and perform complex analyses very quickly and in a fully automated fashion could transform astrophysics in a way that is much needed for future sky surveys. And those will look deeper into the universe—and produce more data than ever before. What are the current uses of machine learning? Now that we know how powerful machine learning can be, it’s inevitable to ask ourselves: Has machine learning in Astronomy been deployed for something useful already? The answer is… kind of. The truth is that the application of machine learning in astronomy is very much a novel technique. Although astronomers have long used computational techniques, such as simulations, to aid them in research, ML is a different kind of beast. Still, there are some examples of the use of ML in real life. Let’s start with the easiest one. Images obtained from telescopes often contain “noise”. What we consider as noise are any random fluctuations not related to the observations. For example, wind and the structure of the atmosphere can affect the image produced by a telescope on the ground as the air gets in the way. That is the reason we send some telescopes to space – to eliminate the influence of Earth’s atmosphere. But how can you clear the noise produced by these factors? Via machine learning algorithm called a Generative Adversarial Network or GAN. GANs consist of two elements – a neural network that tries to generate objects and another one (a “discriminator”) that tries to guess whether the object is real or fake-generated. This is an extremely common and successful technique of removing noise, already dominating the self-driving car industry. In astronomy, it’s very important to have as clear of an image as possible. That’s why this technique is getting widely adopted. Another example of AI comes from NASA. However, this time it has non-space applications. I am talking about wildfire and flood detection. NASA has trained machines to recognize the smoke from wildfires using satellite images. The goal? To deploy hundreds of small satellites, all equipped with machine-learning algorithms embedded within sensors. With such a capability, the sensors could identify wildfires and send the data back to Earth in real-time, providing firefighters and others with up-to-date information that could dramatically improve firefighting efforts. Is there anything else? Yes – NASA’s research on the important application of machine learning in probe landings. One technique for space exploration is to send probes to land on asteroids, gather material and ship it back to Earth. Currently, in order to choose a suitable landing spot, the probe must take pictures of the asteroid from every angle, send them back to Earth, then scientists analyze the images manually and give the probe instructions on what to do. This elaborate process is not only complex but also rather limiting for a number of reasons. First of all, it is really demanding for the people working on the project. Second of all, you should keep in mind that these probes may be a huge distance away from home. Therefore, the signal carrying the commands may need to travel for minutes or even hours to reach it, which makes it impossible to fine-tune. That is why NASA is trying to cut this “informational umbilical cord” and enable the probe to recognize the 3D structure of the asteroid and choose a landing site on its own. And the way to achieve it is by using neural networks. What obstacles and limitations lie ahead for machine learning in Astronomy? If machine learning is so powerful why has it taken so long for it to be applied? Well, one of the reasons is that in order to train a machine learning algorithm you need a lot of labeled and processed data. Until recently, there just wasn’t enough data of some of the exotic astronomical events for a computer to study. It should also be mentioned that neural networks are a type of black box – we don’t have a deep understanding of how they work and make sense of things. Therefore, scientists are understandably nervous about using tools without fully understanding how they work. While we at 365 Data Science are very excited about all ML developments, we should note that it comes with certain limitations. Many take for granted that neural networks have much higher accuracy and little to no bias. Though that may be true in general, it is extremely important for researchers to understand that the input (or training data) they feed to the algorithm can affect the output in a negative way. AI is learning from the training set. Therefore, any biases, intentionally or unintentionally incorporated in the initial data, may persist in the algorithm. For instance, if we think there are only 3 types of galaxies, a supervised learning algorithm would end up believing there are only 3 types of galaxies. Thus, even though the computer itself doesn’t add additional bias, it can still end up reflecting our own. That is to say, we may teach the computer to think in a biased way. It also follows that ML might not be able to identify some revolutionary new model. Those factors are not game-changing. Nevertheless, scientists using this tool need to take these into account. So, what comes next for machine learning? The data we generate increasingly shapes the world we live in. So, it is essential that we introduce data processing techniques (such as machine learning) in every aspect of science. The more researchers start to use machine learning, the more demand there will be for graduates with experience in it. Machine learning is a hot topic even today but, in the future, it is only going to grow. And we’re yet to see what milestones we’ll achieve using AI and ML and how they will transform our lives. References https://365datascience.com/career-data-science-ultimate-guide/ https://365datascience.com/introduction-machine-learning/ Previously published at https://365datascience.com/machine-learning-astronomy/

How Machine Learning is Used in Astronomy

AI issues — open AI & ethics — that require attention Civilizational issues — space war, privacy, us v. them — that require action Technologies — telemedicine & DNA storage — that need to be accelerated ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ AI issues that require attention OpenAI cofounder Greg Brockman on the transformative potential of artificial general intelligence — “[3] things that power all AI systems: data, compute, and algorithms**….** [Now] it’s all about…rare, precious labeled datasets…[but] starting to see models… able to consume unlabeled data…that can read the internet and these massive corpora…. [T]he largest AI training runs have been doubling every 3.5 months at a 300,000 times increase…kind of like if in six years, smartphone batteries went from lasting one day to lasting 800 years…. [T]he algorithms [so far]…involved simple ideas…and we’re going to be in that fog until the landscape stops changing so rapidly…. [Clearly] artificial general intelligence has the potential to cause extremely rapid change…[so] hard for the policy machinery and social norms — how people relate and fit into the system — to keep up. The most important thing for governments… is developing ways to measure it…partly because the present is so poorly understood…. . [Today] it’s hard to separate out the signal from the noise — even for people inside the field… and what’s going to happen in the next few years…[is] an extremely hard problem ….Even if the data is good, if these systems are set up in the wrong way…[or] given the wrong goals…they can end up behaving in surprising ways. So how do you make sure that AI systems do what you intend and operate without bias…are all open questions… .” But more powerful systems that take actions on our behalf are less well-explored…[so] important for us to get out ahead of the problem and not play catch-up https://venturebeat.com/2018/06/25/openai-cofounder-greg-brockman-on-the-transformative-potential-of-artificial-general-intelligence/ The future of AI relies on a code of ethics — “[I]n the near future, almost all new technology will incorporate some form of AI or machine learning… inevitably causing many ethical issues…[whose] impact will be far-reaching — affecting everyone**….** …. [Need] a code of AI ethics to guide us through these upcoming breakthroughs and inevitable dilemma [like] …. The ethics of driverless cars what safety and mobility trade-offs are inherent…. Should these algorithms be transparent? ….[ ….[ ! The battle against fake news The] news media and social platforms…[are] already seeing AI being used to create and defend against political propaganda H]ow much trust we can put in the systems…and how much power we can give them… to prevent them from being misused….[T]he equivalent of Underwriters Laboratories (UL) for news ?…. The future of the automated workplace ….[M]any companies will have to account for the jobs lost…[and] be utilized in other areas…. Will it be the government, employers or automation companies No one will be left untouched. ….[We’ve] reached the AI tipping point…between an AI dreamland and an AI nightmare…[and] need to closely monitor it and ask the right questions as the industry evolves…[to] ensure we’re using the technology responsibly.” https://techcrunch.com/2018/06/21/the-future-of-ai-relies-on-a-code-of-ethics/ Civilizational issues that require action The New Arms Race Threatening to Explode in Space — “[2007] Chinese…shot a satellite out of the sky…a wake-up call…[since] all those satellites overhead had become…billion-dollar sitting ducks…[creating] secretive, pitched arms race…to devise more and better ways to quickly cripple your adversary’s satellites….[and] relearn how to fight ‘unwired’ …. . [Any] space war could…end with a crippled global economy, inoperable infrastructure…[that] might be years before [restored]….Preparing for orbital war has fast become a priority…[as is] figuring out how to prevent it… …. ’When you look at our American way of war, the strategy is largely underpinned by space assets — navigation, early warning, timing’…. And that’s just the military ….[I]n the early moments of a war, it’s a fair bet that satellites…could be among the first targets …. [But] space is already like a war zone: It’s increasingly shot through with flying shrapnel …many too small to be tracked….[There’s] a threshold — called the Kessler effect — that triggers a runaway cascade of collisions In 2014, a piece of presumptive space junk…[was] an autonomous spacecraft capable of veering off course and sidling up to other objects…as Satellite Killer…[ There’s] four categories of space weapons: kinetic (aimed at destroying a satellite) nonkinetic (aimed at disabling a satellite without touching it) electromagnetic (aimed at interfering with a satellite’s signals) …. cyber (aimed at corrupting the data sent to a satellite)…[and] delivers false information ’ …. Because space assets are so expensive…there’s no backup or excess capability’….[T]he 1967 Outer Space Treaty, which banned weapons of mass destruction in space…is still in force, but it is by now full of holes [ W]hat counts as…territorial aggression…[or] a proportional response? It’s even difficult to say…what the physics of war in space will look like.” https://www.wired.com/story/new-arms-race-threatening-to-explode-in-space/ AT&T collaborates on NSA spying through a web of secretive buildings in the US — “[Currently] eight facilities… giving the agency direct ‘backbone’ access to raw data…including emails, web browsing, social media and any… unencrypted online activity… for a surveillance operation code-named FAIRVIEW…[that] only coordinates directly with AT&T…. [ .” But] monitoring what it calls the ‘peering circuits’ at the eight sites, the spy agency can collect ‘not only AT&T’s data, they get all the data that’s interchanged between AT&T’s network and other companies’…[and] process vast quantities of international traffic https://techcrunch.com/2018/06/25/nsa-att-intercept-surveillance/ Why Our Brains See the World as ‘Us’ Versus ‘Them’ — “ [All] brains balance two primordial systems. … [T]he amygdala that can generate fear and distrust…. [T]he mesolimbic system, can give rise to pleasure and feelings of reward in response to things_…._ [M]any people harbor an implicit preference for their in-group…even when they show no outward or obvious signs of bias ….[Studies] found increased signaling in the amygdala when people make millisecond judgments of ‘trustworthiness’… too short a time to reflect conscious processes and likely reveal implicit fears ….[Usually] ‘executive control’ regions can override the amygdala’s push toward prejudice …. …. [T]he mesolimbic system…[releases] dopamine [and]…[oxytocin to] alter the sense of reward and pleasure , especially in relationship to social interactions …. s…. The neural circuits that govern social behavior and reward arose early in vertebrate evolution Activity in our brains is malleable, allowing higher-order circuits in the cortex to modify the more primitive fear and reward systems to produce different behavioral outcome ’ ’ [T]he problem with stereotypes is not that they are untrue, but that they are incomplete…[yet] become the only story …. [Curiously] although diverse individuals are less trusting of one other, when working together, they are more productive… produces better innovation and better solutions than the smartest set of like-minded experts. .” In short, diversity trumps ability…[and] enhances the level of innovation in organizations https://www.scientificamerican.com/article/why-our-brains-see-the-world-as-us-versus-them/ _Is the human species special?_medium.com Doc Says — Our Emotions, Institutions and Technological Capabilities Are Mismatched Technologies that need to be accelerated Let’s make telemedicine available to all — “[Now] telemedicine benefits for many Medicare patients with stroke, kidney disease and other chronic conditions…[should] dramatically increase access to the highest quality of care and [outcomes] …. … …. [Includes] urgent care to virtual visits for follow-up appointments to peer-to-peer consults between physicians…[via] videoconferencing to rapidly evaluate patients and improve door-to-treatment times [and]screenings for eye diseases [For] hospitals’ emergency departments, patients with non-life-threatening conditions can…hold a video consultation with a physician after they arrive, reducing visit times from two to three hours to about 35 to 40 minutes…[and] freeing up emergency rooms and doctors for patients with serious emergencies …. …. [There’s] remote patient monitoring of diabetes care, obstetrics or behavioral health visits…[and] reducing regulatory barriers that limit where the patient must be located to access telemedicine Telemedicine is no longer ‘next-generation’…[but here today and rapidly growing.” https://www.recode.net/2018/6/25/17489494/telemedicine-health-care-virtual-doctor-stroke-videoconference-emergency-medicine-remote-screening The Rise of DNA Data Storage — “DNA is going to have to get a lot cheaper…[but] could be the answer to…information overload. . …. Five years ago humans had produced 4.4 zettabytes of data; that is set to explode to 160 zettabytes (each year!) by 2025 Current infrastructure can only handle a fraction of [that]…[and] expected to consume all the world’s microchip-grade silicon by 2040 ’ DNA has information storage density several orders of magnitude higher than any other known storage technology …[with] every movie ever made…smaller than the size of a sugar cube…[and] last for 10,000 years …. [Problem] is cost….[Perhaps] decoupling the process of writing DNA from…encoding it…[could be] costs competitive with tape…[in] a few years…. .” Molecular data storage has become something…[is] a pet project for [DARPA]…[and] Microsoft plans…an operational prototype storage system…working inside one of its data centers by 2020…and synthetic biology …could actually happen sooner https://www.wired.com/story/the-rise-of-dna-data-storage/ Find more of my ideas on Medium at, . A Passion to Evolve Or click the Follow button below to add me to your feed. Prefer a weekly email newsletter — free, no ads, no spam, & never sell the list — email me with “add me” in the subject line. dochuston1@gmail.com May you live long and prosper!Doc Huston

MICROCHIP TECHNOLOGY INCORPORATED

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10K+ employees

Since 1989

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MICROCHIP TECHNOLOGY INCORPORATED

microchip.com

10K+ employees

Since 1989

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