Google announces its plan to make artificial intelligence more ‘human’

#google announces its plan to make artificial intelligence more ‘human’

  • As AI technology grows rapidly, Google wants to make sure that it’s accessible and inclusive, developed with people in mind.
  • Called the People + AI Research Initiative or the PAIR, this newly-announced team within the Google Brain division will “study and redesign the ways people interact with AI systems” so that we build systems with “people in mind at the start of the process.”
  • The PAIR will examine the relationship between users and technology, the vast array of applications that AI will facilitate, and how to make everything accessible and broadly inclusive.
  • The PAIR team is led by Google Brain researchers Fernanda Viégas and Martin Wattenberg, and the 12 other team members will work with researchers from Harvard University and MIT to focus on three areas of user needs:

    The PAIR won’t necessarily answer all the concerns out there.

  • Compounded by other intersectional factors, the Fourth Industrial Revolution certainly isn’t going to be an easy process, but with Google’s new initiative, we are definitely taking one step further in the right direction: the hope is that as the PAIR team grows and as its work expands, we will see its impact across Google’s apps and services as well as see similar efforts from other companies.

As AI technology grows rapidly, Google wants to make sure that it’s accessible and inclusive, developed with people in mind. That’s where the PAIR comes in.
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Accenture Research: AI Boosts Industry Profits

Artificial Intelligence (#AI) on average can boost company profits by 38% —@pauldaugh

  • — Accenture Research

    The above key findings was derived from comprehensive new research on the potential economic impact of artificial intelligence (AI) in GVA, a close approximation of GDP that accounts for the value of goods and services produced, developed by Accenture Research in collaboration with Frontier Economics.

  • Here are 10 significant takeaways from the new Accenture Research on AI and its potential economic impact:

    1.

  • To prepare for a successful future with AI, business leaders should consider the following eight strategies:

    To further understand the economic impact of AI, Salesforce commissioned a report from IDC on how AI-powered CRM – the fastest growing and soon-to-be largest category of enterprise software – will impact GDP growth and the job market.

  • Here are the key findings:

    Here’s the link to Accenture’s full AI research report.

  • The report found that AI could double annual economic growth rates by 2035 and boost labor productivity by up to 40 percent by fundamentally changing the way work is done.

“Businesses that successfully apply artificial intelligence (AI) could increase profitability by an average of 38 percent by 2035. The introduction of AI…
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A step-by-step guide to building a simple chess AI – freeCodeCamp

A step-by-step guide to building a simple #chess #AI using #Javascript

  • The simplest way to achieve this is to count the relative strength of the pieces on the board using the following table:With the evaluation function, we’re able to create an algorithm that chooses the move that gives the highest evaluation:The only tangible improvement is that our algorithm will now capture a piece if it can.Black plays with the aid of the simple evaluation function.
  • This is done by using the Minimax algorithm.In this algorithm, the recursive tree of all possible moves is explored to a given depth, and the position is evaluated at the ending “leaves” of the tree.After that, we return either the smallest or the largest value of the child to the parent node, depending on whether it’s a white or black to move.
  • The best move for white is b2-c3, because we can guarantee that we can get to a position where the evaluation is -50With minimax in place, our algorithm is starting to understand some basic tactics of chess:Minimax with depth level 2.
  • This helps us evaluate the minimax search tree much deeper, while using the same resources.The alpha-beta pruning is based on the situation where we can stop evaluating a part of the search tree if we find a move that leads to a worse situation than a previously discovered move.The alpha-beta pruning does not influence the outcome of the minimax algorithm — it only makes it faster.The alpha-beta algorithm also is more efficient if we happen to visit first those paths that lead to good moves.The positions we do not need to explore if alpha-beta pruning isused and the tree is visited in the described order.With alpha-beta, we get a significant boost to the minimax algorithm, as is shown in the following example:The number of positions that are required to evaluate if we want to perform a search with depth of 4 and the “root” position is the one that is shown.Follow this link to try the alpha-beta improved version of the chess AI.Step 5: Improved evaluation functionThe initial evaluation function is quite naive as we only count the material that is found on the board.
  • We can decrease or increase the evaluation, depending on the location of the piece.With the following improvement, we start to get an algorithm that plays some “decent” chess, at least from the viewpoint of a casual player:Improved evaluation and alpha-beta pruning with search depth of 3.

At each step, we’ll improve our algorithm with one of these time-tested chess-programming techniques. I’ll demonstrate how each affects the algorithm’s playing style. We’ll use the chess.js library…
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Accenture Research: AI Boosts Industry Profits

Artificial Intelligence (#AI) on average can boost company profits by 38% —@Accenture

  • — Accenture Research

    The above key findings was derived from comprehensive new research on the potential economic impact of artificial intelligence (AI) in GVA, a close approximation of GDP that accounts for the value of goods and services produced, developed by Accenture Research in collaboration with Frontier Economics.

  • Here are 10 significant takeaways from the new Accenture Research on AI and its potential economic impact:

    1.

  • To prepare for a successful future with AI, business leaders should consider the following eight strategies:

    To further understand the economic impact of AI, Salesforce commissioned a report from IDC on how AI-powered CRM – the fastest growing and soon-to-be largest category of enterprise software – will impact GDP growth and the job market.

  • Here are the key findings:

    Here’s the link to Accenture’s full AI research report.

  • The report found that AI could double annual economic growth rates by 2035 and boost labor productivity by up to 40 percent by fundamentally changing the way work is done.

“Businesses that successfully apply artificial intelligence (AI) could increase profitability by an average of 38 percent by 2035. The introduction of AI…
Continue reading “Accenture Research: AI Boosts Industry Profits”

Keep it simple! How to understand Gradient Descent algorithm

Keep it simple! How to understand #GradientDescent algorithm #MachineLearning

  • The blue line gives the actual house prices from historical data (Yactual)

    The difference between Yactual and Ypred (given by the yellow dashed lines) is the prediction error (E)

    So, we need to find a line with optimal values of a,b (called weights) that best fits the historical data by reducing the prediction error and improving prediction accuracy.

  • So, our objective is to find optimal a, b that minimizes the error between actual and predicted values of house price:

    This is where Gradient Descent comes into the picture.

  • Gradient descent is an optimization algorithm that finds the optimal weights (a,b) that reduces  prediction error.
  • Lets now go step by step to understand the Gradient Descent algorithm:

    Initialize the weights(a & b) with random values and calculate Error (SSE)

    Calculate the gradient i.e. change in SSE when the weights (a & b) are changed by a very small value from their original randomly initialized value.

  • Adjust the weights with the gradients to reach the optimal values where SSE is minimized

    Use the new weights for prediction and to calculate the new SSE

    Repeat steps 2 and 3 till further adjustments to weights doesn’t significantly reduce the Error

    We will now go through each of the steps in detail (I worked out the steps in excel, which I have pasted below).


In Data Science, Gradient Descent is one of the important and difficult concepts. Here we explain this concept with an example, in a very simple way. Check this out.

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Practical Deep Learning For Coders—18 hours of lessons for free

Practical #DeepLearning For Coders—18 hours of lessons for free

  • After this course, I cannot ignore the new developments in deep learning—I will devote one third of my machine learning course to the subject.
  • I’m a CEO, not a coder, so the idea that I’d be able to create a GPU deep learning server in the cloud meant learning a lot of new things—but with all the help on the wiki and from the instructors and community on the forum I did it!
  • Sometimes I feared whether I would be able to solve any deep learning problems, as all the research papers I read were very mathy beyond reach of simple intuitive terms.
  • But Jeremy and Rachel (Course Professors) believe in the theory of ‘Simple is Powerful’, by virtue of which anyone who takes this course will be able to confidently understand the simple techniques behind the ‘magic’ Deep Learning.
  • The course exceeded my expectations and showed me first hand how both Deep Learning and ourselves could change the world for better.

fast.ai’s practical deep learning MOOC for coders. Learn CNNs, RNNs, computer vision, NLP, recommendation systems, keras, theano, and much more! neural networks!

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