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10 Ways AI Is Revolutionizing Sales

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Predictive opportunity scoring, predictive lead scoring, predictive analytics for forecast ... [ ] management and guided selling are the top four AI-based technologies B2B selling organizations plan to deploy in the next 12 months, according to Gartner. Sales organizations are under increased pressure to reduce selling costs while stabilizing margins and closing only the most profitable deals. Marketing teams across all industries are under increased pressure to increase the quantity, quality and qualification levels of leads that deliver the highest probability of closing this year. AI-based price and revenue management applications and platforms are proving indispensable in keeping sales, marketing, operations, services, accounting and senior management synchronized with real-time updates to achieve more. McKinsey's Global AI Survey: AI proves its worth, but few scale impact survey provides insights into where AI is making its greatest contributions and reducing expenses.


GPT-3: new AI can write like a human but don't mistake that for thinking – neuroscientist

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Since it was unveiled earlier this year, the new AI-based language generating software GPT-3 has attracted much attention for its ability to produce passages of writing that are convincingly human-like. Some have even suggested that the program, created by Elon Musk's OpenAI, may be considered or appears to exhibit, something like artificial general intelligence (AGI), the ability to understand or perform any task a human can. This breathless coverage reveals a natural yet aberrant collusion in people's minds between the appearance of language and the capacity to think. Language and thought, though obviously not the same, are strongly and intimately related. And some people tend to assume that language is the ultimate sign of thought.



Harvard journal keeps data scientists connected during COVID

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Data science has made key contributions in the battle against COVID-19, from tracking cases and deaths to understanding how populations move during travel restrictions to vaccine design. The Harvard Data Science Initiative is working to support faculty members, students, and fellows in designing and applying the tools of statistics and computer science and creating a community to foster the flow of ideas. The year-old Harvard Data Science Review published a special issue online this summer dedicated to COVID-19 that will be updated with the latest findings, with a goal of fostering innovation and keeping the conversation going about how data science can help meet the COVID-19 challenge. The Gazette spoke with Francesca Dominici, Clarence James Gamble Professor of Biostatistics, Population and Data Science at the Harvard T.H. Chan School of Public Health and co-director of the initiative, and Xiao-Li Meng, the review's editor in chief and the Whipple V.N. Jones Professor of Statistics in the Faculty of Arts and Sciences, about how data science can be used to meet today's challenges, and in turn, challenges facing the field. GAZETTE: How is data science important to our understanding of and response to COVID-19? DOMINICI: Data science is on the front page of The New York Times probably every single day.


10 Azure ML Code Examples Every Cloud AI Developer Should Know

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TLDR; The Azure ML Python SDK enables Data scientists, AI engineers,and MLOps developers to be productive in the cloud. This post highlights 10 examples every cloud AI developer should know, to be successful with Azure ML. If you are new to Azure you can get a free subscription using the link below. The scripts in this example are used to classify iris flower images to build a machine learning model based on scikit-learn's iris dataset the code can easily be adapted to any scikit-learn estimator. This example shows you how to run your TensorFlow training scripts at scale using Azure Machine Learning's TensorFlow estimator class.



Can AI help with your quest for global talent? - Information Age

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The recruitment process has come a long way since the days of paper CVs. Thanks to a decade-long digital transformation, online job sites, virtual portfolios, and even Skype interviews are now staples in global talent acquisition, but could artificial intelligence (AI) elevate the hiring landscape and take the recruitment process one step further? AI has become somewhat of a buzzword lately. When we think of AI, we often think of human-like robots which can mimic our behaviour (and potentially take over the world someday). However, although artificially intelligent robots do exist, the term AI typically applies to any self-learning machine that can analyse data and provide insights that make us smarter, more efficient and better at the things we do every day.


The Minimax Algorithm in Artificial Intelligence

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Algorithms can search the game trees to determine the best move to make from the current state. The most well known is called the Minimax algorithm. The minimax algorithm is a useful method for simple two-player games. It is a method for selecting the best move given an alternating game where each player opposes the other working toward a mutually exclusive goal. Each player knows the moves that are possible given a current game state, so for each move, all subsequent moves can be discovered.


How AI Platforms Are Improving Talent Management In 2020

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Bottom Line: Dexcom and Micron adopting a single AI platform for talent management that adapts to their specific HR strategies and provides new insights is delivering significant results. AI-based platforms provide new insights, intelligence and guidance to CHROs and HR leaders, helping them close the growing talent gaps their organizations face. By integrating hiring, internal mobility, diversity & inclusion, contingent workforces, training & development and performance management all on a single AI platform, HR leaders gain greater insights into closing talent gaps. And it's encouraging to see how AI platforms evaluate candidates on their capabilities while anonymizing factors that might lead to hiring bias. Interested in learning more about why AI platforms are gaining adoption, I recently attended a webinar co-hosted by Talent Tech Lab (TTL) and Eightfold.ai The webinar is titled An AI-First Approach to Recruiting with Eightfold and TTL.


How You Can Use Docker to Setup Machine Learning Environments in Less Than A Minute

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Imagine that you are working on a project, with a team of 10 people. All members of this team, have to work from home now, because of the ongoing pandemic, so all of them have different laptops, different system specifications, different operating systems, etc. Now one fine day, a team member pushes a new change to GitHub, that adds some new functionality to your project. Unfortunately, these new changes do not work for some people, maybe because of different versions of the software installed on the different computers. So you have a very common problem, that many teams often face. "It works for him, but not for me" Docker was made specifically to solve this problem.