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.
Pick an industry – any industry – and you can virtually guarantee that AI will have been hailed as its next big thing. The cryptocurrency sector is no different, with many of 2017's ICOs shoehorning the concept into their whitepapers somewhere in a bid to appear "cutting edge" and in touch with the zeitgeist. But beyond all the hype, what impact will artificial intelligence have on the crypto industry, and could its rise ultimately render human traders obsolete? AI is to tech what "blockchain" is to the cryptocurrency industry: a concept whose genuine applications are significantly outnumbered by the projects interested solely in latching onto the buzzword and surfing it for all it's worth. Given that startups described as being involved with AI attract 15-50% more funding than other tech firms, it's understandable why companies are so keen to cash in on the hype.
Like all good superheroes, every company has its own origin story explaining why they were created and how they grew over time. This article covers the origin story of QuestDB and frames it with an introduction to time series databases to show where we sit in that landscape today. Time series is a succession of data points ordered by time. These data points could be a succession of events from an application's users, the state of CPU and memory usage over time, financial trades recorded every microsecond, or sensors from a car emitting data about the vehicle acceleration and velocity. For that reason, time-series is synonymous with large amounts of data.
Artificial intelligence (AI) has both surpassed and replaced humans in many fields. Will AI overpower humanity in the near or distant future? Will AI control humans and replace governments? Or will AI remain a tool that humans will use to improve their performances? Research on brain-computer interface (BCI) has begun and suggests that we implant chips or connect devices to our brain to increase computing power.
It's been a decade since construction players began embracing digital solutions. In the early- to mid-2010s, thousands of new market entrants offered point solutions that served existing use cases or, in some instances, created new ones. The first widely adopted construction point solutions addressed basic needs; for example, improving design capabilities or digitizing paper-based information. By the second half of the decade, industry players--spurred by end-customer feedback about their difficulty integrating point solutions--began expanding their product portfolios to create suites of integrated solutions. While the construction technology industry is still filled with players offering point solutions or limited suites, our latest annual effort to map and understand the construction technology landscape reveals that the industry is moving toward platforms and predicts that a combination of multiple platforms will coexist in the space.
European investors have said artificial intelligence (AI) is the most compelling long-term thematic opportunity, according to a survey conducted by CoreData Research. The survey, which was commissioned by WisdomTree and interviewed 440 European investors with approximately €240bn AUM, found 71.4% of respondents highlighted AI as the best thematic opportunity while 59.8% said biotech and 46.6% pointed to cyber security. There are four AI ETFs available on the European market with the largest being the $187m WisdomTree Artificial Intelligence UCITS ETF (WTAI). The $168m Amundi STOXX Global Artificial Intelligence UCITS ETF (GOAI) tracks an index from Stoxx while the $158m L&G Artificial Intelligence UCITS ETF (AIAI) offers exposure to a ROBO Global index and the final strategy offers exposure to big data as well as AI, the $104m Xtrackers Artificial Intelligence & Big Data UCITS ETF (XAIX). AI is in its early stages of adoption and application with investors highlighting the technology to transform industries, services, labour and consumption.
CTech – Israeli business intelligence startup Intelligo Group, which has developed an automated due diligence and personnel background platform based on AI (artificial intelligence), has announced a $15 million Series B financing round led by Behrens Investment Group and including several existing investors. Intelligo has raised a total of $22 million to date. The company was founded in 2014 by CEO Shlomo Mirvis, Chief Research Officer Dana Rakovsky and COO Nadav Ellinson. Intelligo currently has more than 100 clients, including top corporations. The company employs 42 people and is in the process of adding to its workforce.
For investors looking for momentum, First Trust Nasdaq Artificial Intelligence and Robotics ETF ROBT is probably a suitable pick. The fund just hit a 52-week high and is up 96.8% from its 52-week low price of $22.51/share. Let's take a look at the fund and its near-term outlook to gain an insight into where it might be headed: This ETF seeks investment results that correspond generally to the price and yield, before the fees and expenses of the Nasdaq CTA Artificial Intelligence and Robotics Index. It has AUM of $137.5 million and charges 65 basis points in annual fees. Due to the coronavirus outbreak, the robotics market is flooded with opportunities as robots are being used for jobs such as sanitizing hospitals, homes and workplaces along with monitoring, surveying, handling, and delivering food and medicines.
The dictates of big data--its inner manipulations and trends--have defined the very form of the data ecosystem since the inception of these technologies nearly a decade ago. It's become entrenched in the most meaningful dimensions of data management, implicit to all but its most mundane practices, and indistinguishable from almost any type of data leveraged for competitive advantage. As such, current momentum in the big data space isn't centered on devising new expressions of its capabilities, but rather on converging them to actualize the long sought, rarely realized, time honored IT ideal of what Cambridge Semantics CTO Sean Martin termed "interoperability. And, the more the data starts to support that, the more interesting that gets, too." The grand vision of interoperability involves the capacity to readily interchange enterprise systems and resources as needed to maximize business productivity without technological restrictions.
This report Added by Market Study Report, LLC, focuses on factors influencing the present scenario of the ' GPU for Deep Learning market'. The research report also offers concise analysis referring to commercialization aspects, profit estimation and market size of the industry. In addition, the report highlights the competitive standing of major players in the projection timeline which also includes their portfolios and expansion endeavors. The GPU for Deep Learning market report is an exhaustive investigation of this business sphere. The report predicts the market renumeration and growth rate over the estimated timeframe.