AutoML enjoys a steadily increasing popularity (see Forbes). Not least driven by the numerous successes in practical analyses. In a world in which more and more devices produce data and are networked with each other, the data "produced" grows disproportionately. Therefore AutoML is of urgent necessity to gain knowledge from these rapidly increasing data on time. We assume that AutoML becomes even more critical in the coming years and that the analysis methods deliver even more precise and faster results. The field of activity of the data scientist will not disappear, but rather, his focus will shift to more specific or sophisticated analysis techniques.
At the start of the 2010s, the hype around Big Data really took off. As the expectations around advanced analytics and analyzing unstructured data grew, the role of "Data Scientist" appeared on the upward slope of the Gartner hype-cycle (see figure below). At the same time, challenges with implementing various important new data platforms referenced in the Gartner graphic were starting to become apparent (i.e., Map Reduce and other distributed systems and Database Platform as a Service), and these start to appear on the downward slope of the same Gartner hype-cycle. These platforms didn't magically provide the Data Scientists with the data they required, and it became clear that a lot of design and engineering was required to align these data-platforms with what the Data Scientists needed. Also, a huge amount of hype and expectation was developing around NoSQL databases, but this mainly focused on the needs of web-scale applications and agile development rather than the needs of data analysis.
Can we integrate the power of Python calculation with a Tableau? That question was encourage me to start exploring the possibility of using Python calculation in Tableau, and I ended up with a TabPy. How can we use TabPy to integrating Python and Tableau? In this article, I will introduce TabPy and go through an example of how we can use it. TabPy is an Analytics Extension from Tableau which enables us as a user to execute Python scripts and saved functions using Tableau.
GPT-3, the latest incarnation of artificially intelligent natural-language systems, knows how to write -- and write and write and write. For a taste of what it can (and cannot) do, here are three examples of its verbosity. In each case, we gave the system a short prompt (in italics) and let it roll. First we asked it to write about itself. Then, playing off a suggestion from a start-up called Sudowrite, which has spent months testing GPT-3, we asked the system to write a Modern Love column.
Zapata Computing has raised $38 million for its quantum computing enterprise software platform. The figure, which brings its total funding to over $64 million, will be put toward Zapata's core mission: "Delivering quantum advantage for customers through real business use cases." Quantum computing leverages qubits (unlike bits that can only be in a state of 0 or 1, qubits can also be in a superposition of the two) to perform computations that would be much more difficult, or simply not feasible, for a classical computer. Unlike most quantum computing startups that build the hardware, Zapata is focused on the algorithms and software that sit on top. Based in Boston, Zapata has one product: its hardware-agnostic Orquestra quantum computing platform.
The COVID-19 pandemic has increased the focus on the use of artificial intelligence (AI) across the life sciences organization, from R&D to manufacturing, supply chain, and commercial functions. During the pandemic, company leadership and management realized that they could run many aspects of their business remotely and with digital solutions. This experience has transformed mindsets; leaders are more likely to lean into a future that lies in digital investments, data, and AI because of this experience. At present, the life sciences industry has only begun to scratch the surface of AI's potential, primarily applying it to automate existing processes. By melding AI with rigorous medical and scientific knowledge, companies can do even more to leverage this technology to transform processes and achieve a competitive edge. AI has the potential to identify and validate genetic targets for drug development, design novel compounds, expedite drug development, make supply chains smarter and more responsive, and help launch and market products. We will highlight a number of these use cases in this report.
Almost a year ago I had the good fortune to get introduced to Dr. Yuping Liu-Thompkins of Loyalty Science Lab, an incredible academician, researcher and good person. Her research focuses on real world issues of more than intellectual interest, research that suggests both problems and solutions. And Dr. Liu-Thompkins, in conjunction with David King and Dr. Bonnie Holub -- both of Teradata-- have done it again. This post might seem to be aimed at retailers -- and to some degree it's a wake up call for them - but I think you'll see it's pointing to important changes in consumer behavior that have occurred during the pandemic - changes that for the most part are likely to survive it. Their case for analytics usage -- which is not the obvious case -- is compelling.
Digital transformation is the flavor of the season. Every company has accelerated its efforts to digitize operations, gather intelligence, and rapidly respond to a changing market. McKinsey senior partner Kate Smaje says that organizations are now accomplishing in 10 days what used to take them 10 months. With data powering better and faster decisions, she says, the road to recovery is paved with data. As a result, most organizations are trying to adopt data-driven decision-making.
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Hyderabad, November 23, 2020 –– Analytics Insight conducted a survey "The Global Artificial Intelligence Trends 2020" to understand the global adoption of Artificial Intelligence (AI) amongst enterprises and recognize the business perceptions of AI across sectors. Analytics Insight reached out to 2,200 professionals online located in different geographic regions across a wide range of industries to explore different views toward AI and its current implications among enterprises. Receiving 256 responses for the survey, Analytics Insight articulated a detailed report, which can be indicative of the market as a whole. Out of the 256 respondents, 48.5% were working at small-scale companies with the company size of fewer than 100 employees. About 29.8% of the respondents were employed at companies which had total employees ranging from 100-1000, while 21.7% of respondents had a company size of over 1000 employees.