AI News, BigML Events
- On Friday, July 6, 2018
- By Read More
In the last decade we have witnessed the phenomenon of Data Deluge, which can be summed up as an unprecedented rise in the volume, variety, veracity and the velocity of data.
The excitement of the promises of this new wave has been dampened by reports of businesses struggling to recoup their investments into heavy infrastructure that store and process such data.
Supervised learning algorithms are trained using labeled examples, such as an input where the desired output is known.
The learning algorithm receives a set of inputs along with the corresponding correct outputs, and the algorithm learns by comparing its actual output with correct outputs to find errors.
Through methods like classification, regression, prediction and gradient boosting, supervised learning uses patterns to predict the values of the label on additional unlabeled data.
Popular techniques include self-organizing maps, nearest-neighbor mapping, k-means clustering and singular value decomposition.
8 Ways Machine Learning Is Improving Companies’ Work Processes
Today’s leading organizations are using machine learning–based tools to automate decision processes, and they’restarting to experiment with more-advanced uses of artificial intelligence (AI) for digital transformation.
Machine learning is enabling companies to expand their top-line growth and optimize processes while improving employee engagement and increasing customer satisfaction.
Here are some concrete examples of how AI and machine learning are creating value in companies today: Other areas where machine intelligencecould soon be commonly used include: Machine learning enables a company to reimagine end-to-end business processes with digital intelligence.
To prepare your enterprise for the future, the first step is to assess your existing information systems and data flows to distinguish the areas that are ready for automation from those where more investment is needed.
The question now isnot about whether managers should investigate adopting AI but about how fast they can do so.At the same time, organizations need to be thoughtful about how they apply AI to their organizations, with a full understanding of the advantages and disadvantages inherent in the technology.
Artificial Intelligence: Implications for Business Strategy (self-paced online)
His research interests include the empirical validation and implementation of financial asset pricing models;
hedge-fund risk and return dynamics and risk transparency;
and, most recently, evolutionary and neurobiological models of individual risk preferences and financial markets.
Lo has published numerous articles in finance and economics journals.
He is founder and chief scientific officer of AlphaSimplex Group, LLC, a quantitative investment management company based in Cambridge, Massachusetts.
Lo’s current research falls into four areas: evolutionary models of behavior and adaptive markets, systemic risk, the dynamics of the hedge funds industry, and healthcare finance.Current projects include: deriving risk aversion, loss aversion, probability matching, and other behaviors as the product of evolution in stochastic environments;
constructing new measures of systemic risk and comparing them across time and systemic events;
applying spectral analysis to investment strategies to decompose returns into fundamental frequencies;and developing new risk management tools for drug discovery and financing methods for funding biomedical innovation.
- On Monday, March 25, 2019
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