"The problem of giving rules for producing true scientific statements has been replaced by the problem of finding efficient heuristic rules for culling the reasonable candidates for an explanation from an appropriate set of possible candidates [and finding methods for constructing the candidates]."
– B. Buchanan, quoted in Lindley Darden. Recent Work in Computational Scientific Discovery.
Sifting effectively through this vast chemical space would allow us to rapidly find a specific molecule and create a new material with the properties we want. This could unlock endless possibilities of material design – for life-saving drugs, better batteries, more advanced prosthetic limbs or faster and safer cars, advancing healthcare, manufacturing, defense, biotechnology, communications and nearly every other industry. This design ability would replace our centuries-old reliance on serendipity in material discovery – something we've been through with plastics, Teflon, Velcro, Vaseline, vulcanized rubber and so many other breakthroughs. Even graphene – the atom-thick layer of carbon and the thinnest, strongest material known – was discovered by (informed) chance, when physicist Kostya Novoselov found discarded Scotch tape in his lab's waste basket.
Impact Biomedical, a wholly-owned subsidiary of SGX-listed Singapore eDevelopment, has announced the initiation of Quantum, a research program designed as a solution to the'patent cliff', the impending pharmaceutical threat. A patent cliff looms when patents for blockbuster drugs expire without being replaced with new drugs, and pharmaceutical companies experience an abrupt decrease in revenue, reducing overall pharmaceutical innovation globally, including crucial research into new methods to prevent and treat illnesses. Impact, through their strategic partner Global Research and Discovery Group Sciences (GRDG), has created a solution called Quantum, a new frontier in pharmaceutical development. Quantum is a new class of medicinal chemistry that uses advanced methods to boost efficacy and persistence of natural compounds and existing drugs while maintaining the safety profile of the original molecules. Instead of modifying functional groups, as is typically done presently in drug discovery, this new technique alters the behavior of molecules at the sub-molecular level.
We talk a lot about speed and capacity when it comes to 5G. But some of its greatest potential lies in a capability we haven't seen before: Distributed intelligence. In the same way that smartphones ignited the app economy and changed how we live, the next ...generation of wireless networks will make AI applications accessible to any connected device.
It's time to reset, re-create and collaborate on a new paradigm where Compassion and Kindness are the prevailing norms, one where technology is a tool for making humans more humane and creating an Abundant world for the majority. Join us to turn this vision into reality. Let's look into the'White Mirror' … Inspired by Black Mirror (Netflix series) - 'White Mirror' (holding name while we devise a suitable one) provides an immersive flash forward (glimpse/vision) of our Utopian future. In uncertain times (like now), technological disruption and impactful stories can change our mental worldview - our perceptions and eventually our reality. Black Mirror is a powerful show, depicting a dystopian future caused in part by misused evolving technologies.
The confidence intervals are the type of estimate which give us an estimation of where the parameters are located. Nonetheless, when we have to make a decision we need a'yes' or'no' answer, to do so we will perform a test known as Hypothesis Testing. Steps in data-driven decision making.: A hypothesis is an idea that can be tested. For example, apples in London are expensive.
I scrambled up a ladder to the tin roof of our house, clutching a book about the evolution of animals. I was 10 years old, and I'd just finished cooking dinner for my entire family—a task that was my daily responsibility. From my perch, I could look out at the slum where we lived in a small town in India. But that wasn't what drew me to the roof: We didn't have any lamps in our house, so I needed sunlight to read my book. I didn't know it at the time, but that study routine was my ticket to a career as a scientist. > “I hope others can take inspiration from my story and realize … they, too, can persevere.” My father—a laborer—didn't let me attend school initially. I was always jealous of my younger brother when he set off to school each day. So, one day, when I was 5 years old, I followed him and hid under the teacher's desk. She noticed me and sent me home. But the next day, she called my father and told him that he should put me in school. Much to my delight, my father said yes. I had a passion for learning, and—despite the hunger pangs I went to school with most days—I quickly shot to the top of my class. When I was 10 years old, my father sent me to a better school outside our neighborhood, one that was mostly attended by students from wealthier families. I was at the top of the class there, too. But I was treated poorly by classmates who saw me as a child of the slums. I also suffered from embarrassment during biology labs because I was very short—due to malnutrition, I suspect—and I had to stand on a chair to see into the microscope. When I graduated from high school, I wanted to become an engineer. My father was eager for me to attend university, but he told me I couldn't study engineering because it was for boys; he said I should study food science instead. My initial reaction was that food science was the last thing I wanted to study. After a childhood preparing meals for my family, there was nothing I hated more than cooking. I enrolled in a food science program anyway, and I quickly discovered that food science wasn't so bad after all. It was a real science—something akin to chemistry—that involved hypothesis testing and experimentation. Soon enough, I was hooked. While attending university, I lived in a hostel near campus, paying my tuition and living expenses with the help of student loans my father secured for me as well as my side job as a research assistant. My room had a lamp, and I was thankful every night that I had light to study under—something I have learned to never take for granted. In the years that followed, I received a Ph.D. in food engineering and was appointed to a faculty position—milestones that felt far removed from my beginnings in the slums. But shortly thereafter, I began a collaboration that brought me back to my roots. I worked with a company that wanted to tackle malnutrition in India's slums. When representatives from the company first approached me, they said, “You'd need to go to the slums and talk with people”—thinking that I'd never done that before. “That's no problem,” I replied. “I grew up in the slums.” As part of my work with the company, I modified the ingredients in a traditional Indian flatbread called chapati, which I'd made every day growing up. I realized it was the perfect vehicle to introduce more nutrition into the diet of poor people, because it was a staple eaten at every meal. I experimented with the ingredients and landed on a recipe that replaced wheat flour with cheap, locally grown grains that contain more minerals, protein, and dietary fiber. Other researchers laughed at me when I started to work on chapati because they didn't think there'd be much science, or innovation, associated with it. But I've since proved them wrong. My work has won numerous national and international awards, and companies, nonprofit organizations, and government agencies have all sought my expertise. In my life, I've faced poverty, hunger, and discrimination. But I didn't let them hold me back. I pushed through the obstacles and learned lessons from them that helped propel me forward. I hope others can take inspiration from my story and realize that—despite the challenges they may be facing—they, too, can persevere.
DuPont has a rich history of scientific discovery that has enabled countless innovations and today, we're looking for more people, in more places, to collaborate with us to make life the best that it can be. DuPont Pioneer is aggressively building Big Data and Predictive Analytics capabilities in order to deliver improved services to our customers. We seek a strong data scientist with a background in math, statistics, machine learning and scientific computing to join our team. This is a critical position with the potential to make immediate, significant impact on our business. The successful candidate will have an extensive background in statistical computing and machine learning through courses or thesis/dissertation, and proven experience validating models against experimental data.
Late Apple co-founder and CEO Steve Jobs stressed the importance of serendipity in Silicon Valley, by which he meant chance, unplanned encounters in person between tech employees. His successor, Tim Cook, Monday held a virtual conference for developers in front of the empty seats of the Steve Jobs Theater on Apple's Cupertino campus. The late Steve Jobs told his biographer, Walter Isaacson, that when he commissioned the headquarters for the animated film studio Pixar, in East Bay, Jobs made sure it was an open structure, where everything converged on an atrium. Jobs believed, as Isaacson described it, that creativity is a result of serendipity. Serendipity, meaning, discoveries that happen as a result of chance encounters, is the exact term he used, and to Jobs, it meant in-person meetings.
Berkeley Lab researchers (clockwise from top left) Kristin Persson, John Dagdelen, Gerbrand Ceder, and Amalie Trewartha led development of COVIDScholar, a text-mining tool for COVID-19-related scientific literature. A team of materials scientists at Lawrence Berkeley National Laboratory (Berkeley Lab) – scientists who normally spend their time researching things like high-performance materials for thermoelectrics or battery cathodes – have built a text-mining tool in record time to help the global scientific community synthesize the mountain of scientific literature on COVID-19 being generated every day. The tool, live at covidscholar.org, The hope is that the tool could eventually enable "automated science." "On Google and other search engines people search for what they think is relevant," said Berkeley Lab scientist Gerbrand Ceder, one of the project leads.
A U.S. Department of Energy initiative could refurbish existing supercomputers, turning them into high-performance artificial intelligence machines. WASHINGTON, D.C.--The U.S. Department of Energy (DOE) is planning a major initiative to use artificial intelligence (AI) to speed up scientific discoveries. At a meeting here last week, DOE officials said they will likely ask Congress for between $3 billion and $4 billion over 10 years, roughly the amount the agency is spending to build next-generation "exascale" supercomputers. "That's a good starting point," says Earl Joseph, CEO of Hyperion Research, a high-performance computing analysis firm in St. Paul that tracks AI research funding. He notes, though, that DOE's planned spending is modest compared with the feverish investment in AI by China and industry.