If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
One statistic that most traders have come across numerous times when doing research is that 90% of traders fail to make money in the market, regardless of the asset class they chose to trade. Multiple studies carried out on the subject, and although they do not appear to show that 90% of traders lose money, what is evident is that a majority of traders end up losing their capital. The European Securities and Markets Authority carried out studies that show 76.3% of traders losing money, data from the North American Securities Administrators Association – NASAA that show 70% losing on crypto and Spanish CNMV that shows 75% of traders lose on crypto. These statistics are damning and can scare potential cryptocurrency traders from getting involved in the markets. Our trade automation technology provides a solution that protects traders.
All the sessions from Transform 2021 are available on-demand now. Conversational messaging platform Gupshup today announced that it raised $240 million led by Tiger Global Management, with participation from Fidelity Management, Think Investments, Malabar Investments, Harbor Spring Capital, and others. The tranche, which values the company at $1.64 billion, will be used to build new tools, infrastructure, and services while expanding Gupshup's global reach, CEO Beerud Sheth said. The pandemic has accelerated digital transformation for enterprises, in some cases driving the need for messaging services. Offline businesses are moving online while online businesses are offering more products, and both are looking for tools to better engage customers.
In the previous blog, we discussed a predictive model for house prices using Machine Learning algorithms. In this blog, we are going to discuss the time series forecasting on Zillow economics data using a statistical modeling approach. The project was implemented in September 2019 and forecasting of house prices was done for the next year that is 2020. The code could be reused by changing the span of forecasting that is year for forecasting or duration of forecasting. The results discussed in this blog are for the year 2020.
"We are probably in the second or third inning." Lo, a professor of finance at the MIT Sloan School of Management, and Ajay Agrawal of the University of Toronto's Rotman School of Management shared their perspective at the inaugural CFA Institute Alpha Summit in May. In a conversation moderated by Mary Childs, they focused on three principal concepts that they expect will shape the future of AI and big data. Lo said that applying machine learning to such areas as consumer credit risk management was certainly the first inning. But the industry is now trying to use machine learning tools to better understand human behavior.
Cassie, the ostrich-inspired bipedal robot has crossed a new milestone by traversing a distance of 5 kilometres in an outdoor environment in under an hour, untethered and on a single battery charge. According to its inventors, including robotics professor Jonathan Hurst from Oregon State University (OSU) in the US, Cassie is the first two-legged robot to use machine learning to control a running gait on outdoor terrain. One of the biggest challenges in designing bipedal robots, the researchers explained, is because running requires dynamic balancing – the ability to maintain balance while switching positions or otherwise being in motion. In the case of Cassie, whose knees bend like an ostrich's, they said the robot taught itself to run using a machine learning algorithm that helped it make infinite subtle adjustments to stay upright while moving. "The Dynamic Robotics Laboratory students in the OSU College of Engineering combined expertise from biomechanics and existing robot control approaches with new machine learning tools," Mr Hurst said in a statement.
Google search, Facebook news feed, Amazon product recommendations are obvious examples of digital services used by billions of consumers everyday that successfully leverage Machine Learning (ML)¹. In fact you could say that the stellar growth these companies have experienced over the last decade or more just would not be possible without it. The internet giants have each conquered specific segments of consumers' daily digital lives and are now an ever-present habit for billions of people around the world. Google enables people to discover knowledge and information about products, places and things. Facebook enables people to engage with friends who have similar interests and stories.
My wife and I were recently driving in Virginia, amazed yet again that the GPS technology on our phones could guide us through a thicket of highways, around road accidents, and toward our precise destination. The artificial intelligence (AI) behind the soothing voice telling us where to turn has replaced passenger-seat navigators, maps, even traffic updates on the radio. How on earth did we survive before this technology arrived in our lives? We survived, of course, but were quite literally lost some of the time. My reverie was interrupted by a toll booth. It was empty, as were all the other booths at this particular toll plaza.
DataRobot announced that it is acquiring Algorithmia -- a machine learning operations platform -- and is wrapping up a $300 million Series G funding round as it tries to dominate the augmented intelligence market. DataRobot said the acquisition would help bolster the company's capabilities "to unlock value from AI through better, faster, frictionless solutions for every part of the modern enterprise." Dan Wright, CEO of DataRobot, said Algorithmia's people and technology significantly enhance the company's mission to move from experimental to applied Topic: AI rapidlyto rapidly move from experimental to applied AI by helping customers bring every model into production with rapid time to value. "We are thrilled to welcome the Algorithmia team and advance our leading MLOps offering with world-class, enterprise-grade MLOps infrastructure to organizations across the globe," Wright said. They plan to integrate Algorithmia's focus on model serving with DataRobot's model monitoring and model management capabilities.