Exploring the Data Science Evolution

first_imgArtificial Intelligence (AI), once the specter of Computer Science labs, is finding true physical form today. Thanks to the likes of Siri, Alexa, and “Hey Google,” Natural Language Processing has become a valuable assistant in our daily lives. AI also opens the door to new patient insights, inventory reductions, and fraud mitigation. The advent of quick, cheap, dense compute along with ubiquitous, large-scale storage create the perfect conditions. The invention of a new scientific discipline known as modern data science brings purpose and clarity to these technological advances. Modern data science has transformed to fit the native runtime architectures like a lock and proverbial key. While the fundamentals of scientifically analyzing data have not changed, it has transformed into a discipline that models and implements modern solutions for AI problems that were tremendously complex prior to the mid 1980s. As a result, anyone who wishes to be an effective AI practitioner today can achieve solid capabilities through data science.Data Science Evolves into the FuturePast: Data? Science? What?Once upon a time, there was scattered data on office shelves and corporate databases. Science was found in physics, chemistry, mathematics, and biology academic departments. The research team was focused on their isolated line of scientific interest. A “scientist” visualized a novel progressive idea, gathered existing information from previous experiments, and proposed a model for its implementation. Upon a committee’s approval, throughout this slow one-person show, she organized the data, cleansed it, perhaps modeled it in a mathematically defined system and started producing results. There was seldom a computer involved in processing that data. The invisible relationship between the “data” and the “science” that leveraged its value was implicit in the scientist’s mind. And the show went on: progress was made with nearly no automation.Present: A Star is RebornIn order to deliver Artificial Intelligence solutions today, a data scientist must have in-depth knowledge in multiple data skill sets such as Computer Science, Calculus & Statistics, Coding (Python or R), Data Wrangling, Machine Learning, Data Visualization, Communication Skills, and Business Domains. By creating an inference schema utilizing these skills, we can visualize a brand new scientific super-space. This modern analytics paradigm contains all the enablement aspects of the basic data skill sets and produces a single discipline known to us today as “Data Science.”  However, mastering these skills in order to become a data scientist is challenging. Automation of scientific tasks not only simplifies the overall implementation pipeline, but also contributes to improving the solution’s accuracy. Existing Data Science automation tools such as DataRobot, Noodle.ai and advancements in open source ML tools present tedious hurdles that must be overcome. A data scientist who achieves this can turn these knobs and eventually land on a concrete performing model. To have direct access to an algorithm’s granular knobs, data scientists prefer to code implementation details and leverage optimized available frameworks to build, validate and test a model. This is the emerging modern trend.Future: Imagining a Biologically Inspired IntelligenceFor decades, some in the scientific community have dreamed of eliminating the need to place “Artificial” next to “Intelligence.” The intent has been to replace the stochastical optimization processes of statistical ML models mentioned above with biological mechanisms that the living organisms develop genetically. This is called Evolutionary Learning, and an artifact of this is referred to as Evolutionary Intelligence. The two approaches differ in terms of programmability versus adaptability. Modern data science, described above, is a “programmable” approach. An adaptable organism references its genetic pool as a database, mutates a genes row replica and materializes a customized genetic row used in development of new cells while growing physical organelles. This is evolution’s natural selection and mutation process.We have not even come close to replicating this in a lab. Critically, we lack the technology to manufacture synthetic material that mimics human brain tissues’ molecular structure operating at exaFlops equivalent (10^18 operations per-second). The moment we achieve this, human-like brains will be exposed to environmental changes (in-progress training data) in an ecosystem and start learning on their own.  We can then claim that adaptable human-brain-like “object recognizer” machines have been built which function completely opposite to the existing Silicon based “number crunchers” known as modern computers. They will self-assemble to learn from our living environments and naturally adapt to the changes in a developing evolutionary training process.ConclusionChoosing the right AI solutions can be daunting, so we’ve created an ecosystem of resources to help customers on their journey. Find more information here, and if you’re ready to get started, contact our Emerging Tech team.last_img read more

Read More »

Road Trip

first_imgLike many other young people, plant pathology graduate student Russell Ingram’s friends have an epic road trip planned for this summer. The difference is that instead of setting off for a music festival in the desert or visiting a beach, Ingram’s pals are hitting the road in search of jobs.About two-dozen graduate students from the University of Georgia College of Agricultural and Environmental Sciences (CAES) will set off this July for the breadbasket of America in order to meet with potential employers and research partners.Ingram and his fellow students know that a degree by itself, even a graduate degree, isn’t enough to land the job of your dreams. They’re going straight to potential employers to find out what they want from potential applicants. Think of it as a career fair, but in reverse, Ingram said.The students, who are studying in the departments of entomology, plant pathology and crop and soil sciences, will travel by bus July 18–21 to visit the headquarters of Dow AgroSciences in Indianapolis, Indiana, and the Purdue University Crop Diagnostic Training and Research Center for their midseason diagnostic workshop.“Agricultural companies such as Dow provide some of the top-paying and most sought-after jobs in industry,” Ingram said. “I thought the most efficient means of learning the wants and needs of the industry was to go straight to the source and let them tell us what they are looking for in a potential employee.“The added benefit to visiting the Dow campus is that students will have the opportunity to meet with (human resources) staff, the summer internship committee and crop protection leaders to discuss internship and job opportunities. The ultimate goal for this trip will be to help at least one student get an internship or a job offer.”Ingram secured enough grant money so that each student is only paying $50 for the four-day trip. Dow provided $3,500 in funding through its Dow Aid-to-Education program. He also collected about $5,000 in funding through the departments represented and from a grant provided by the Society of Aspiring Plant Pathologists.“These graduate students are to be commended for taking the initiative to travel to the headquarters of Dow AgroSciences to learn about career opportunities,” said Jean Bertand, the assistant dean for instruction at CAES, who was instrumental in securing the grant funding that made the trip a reality. “They are positioning themselves to be well-informed, which will serve them well when it is time to get a job.”To follow along on their road trip visit blog.caes.uga.edu/cropprotection or follow the students on Twitter @UGACropCareers.For more information about the departments of entomology, plant pathology and crop and soil sciences and other graduate programs at CAES, please visit students.caes.uga.edu/graduate.html.last_img read more

Read More »

Fantasy Football TE Sleepers: Ian Thomas, Irv Smith Jr., and Taysom Hill (?) among potential breakout tight ends

first_imgThe tight end position features only a handful of truly elite options heading into 2020. Travis Kelce, George Kittle, Darren Waller, Zach Ertz, and Mark Andrews were the only five tight ends who scored more than 200 fantasy points last year. Fantasy football owners who cannot land one of those five options (and Evan Engram and Hunter Henry) must be on the lookout for the right TE sleeper or potential breakout later in their draft, even if that means drafting a backup.  Here’s a closer look at nine potentially undervalued TEs, from those who will go in the middle rounds to those who might not be drafted at all.  2020 Fantasy Sleepers:Quarterback | Running back | Wide receiverFantasy Football TE SleepersHayden Hurst, Falcons  Hurst won’t have to share targets in Atlanta as the main option, and that increased role should lead to more production with Matt Ryan. Hurst’s reliability is a huge plus. He had 43 catches on 62 targets – a catch percentage of 69.3 — the past two seasons. Expect career highs in receptions, yards and TDs, perhaps even on par with Austin Hooper’s averages the past two seasons in Atlanta (73 catches, 92.5 targets, 723.5 yards, five TDs). Jonnu Smith, Titans Smith is another reliable target, catching 35-of-44 targets last season. There is a boom-or-bust-factor at work, as Smith also had four games with no catches in 2019 and relied on big plays in other games to pad his stats. Still, the increased consistency in the second half of the season showed his potential, and with Delanie Walker officially out of town, he should see a big uptick in targets. Given his explosiveness, that could mean big things at a traditionally touchdown-reliant position.Mike Gesicki, Dolphins  Rookie quarterback Tua Tagovailoa is going to need a safety valve when he eventually takes over, and Gesicki fits that description heading into his third season. Gesicki put up 20 catches, 49.6 yards per game and three TDs in Miami’s final five games in 2019 – a consistent run of production that would make him an every-week starter over a full season.  Ian Thomas, Panthers Thomas put up modest totals with revolving-door quarterbacks in his first two seasons with the Panthers, but the addition of Teddy Bridgewater — and more important, the official exit of Greg Olsen — gives the third-year tight end a new-found opportunity. Thomas might be a better fit in PPR leagues early in the season, and he’ll need to score more TDs to be an every-week option. Still, he won’t cost much on draft day and has big upside in Carolina’s ball-control offense.  Chris Herndon, Jets  Herndon was a legitimate sleeper pick heading into 2019, but he missed most of last season because of suspension and a fractured rib. He averaged 12.9 yards per catch with four TDs as a rookie, and the key will be recapturing that rapport with third-year quarterback Sam Darnold. Some of that sleeper shine has worn off Herndon, but that could make him a sneaky post-hype value pick in the later rounds.  Blake Jarwin, Cowboys  Jarwin gets the chance to emerge in Dallas’ offense now that Jason Witten, who signed with the Raiders, is out of the picture. That means more receptions and yards, but that’s not the biggest upside to taking Jarwin. Seven of Jarwin’s 31 receptions last season went for 20 yards or more, and he will continue to be a down-field threat and red-zone target given the receiving talent the Cowobys have around him.   Irv Smith Jr., Vikings  Smith had two games with more than 50 yards receiving as a rookie, but he is poised to make the jump in his second season. A 76.6 catch percentage shows that Smith makes the most of his targets. Smith still is fighting for looks with veteran Kyle Rudolph, however, so the key will be taking advantage of red-zone opportunities and converting big plays when given the chance. Given his athleticism, Smith Jr. has major upside.  C.J. Uzomah, Bengals Uzomah’s production slipped last season, but the good news is he no longer has to split targets with Tyler Eifert, who left for Jacksonville. That, coupled with the addition of rookie quarterback Joe Burrow – should lead to a more prominent role in the offense. If Uzomah can revert to his 2018 form and tack on a few more TDs, then he will emerge as at least a streaming option in standard leagues.  Taysom Hill, Saints Hill is now listed as a tight end/flex play in ESPN leagues for 2020, and that adds to his switch-blade appeal in any format. Hill had 19 catches on 22 targets last season, and he scored seven TDs on just 46 offensive touches. Hill had more than 50 total yards in just one game, but the TD appeal at the position is nice.last_img read more

Read More »