Now Google is using AI to design chips, far faster than human engineers can do the job

AI chip design

A team of researchers from Google has unveiled a new AI model that can come up with complex chip designs in hours – a burdensome, intricate task that typically takes months for human engineers to complete.  The researchers used a dataset of 10,000 chip layouts to feed a machine-learning model, which was then trained with reinforcement learning. It emerged that in only six hours, the model could generate a design that optimizes the placement of different components on the chip, to create a final layout that satisfies operational requirements such as processing speed and power efficiency. 

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8 alternatives to TensorFlow Serving

Tensorflow

TensorFlow Serving is an easy-to-deploy, flexible and high performing serving system for machine learning models built for production environments. It allows easy deployment of algorithms and experiments while allowing developers to keep the same server architecture and APIs. TensorFlow Serving provides seamless integration with TensorFlow models, and can also be easily extended to other models and data. 

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AI ‘dominated scientific output’ in recent years, UNESCO report shows

Unesco

The United Nations Educational, Scientific, and Cultural Organization (UNESCO) today unveiled its latest Science Report. The massive undertaking — this year’s report totals 762 pages, compiled by 70 authors from 52 countries over 18 months — is published every five years to examine current trends in science governance. This latest edition includes discussion of the rapid progress toward Industry 4.0 and, for the first time, a deep analysis of AI and robotics research around the globe.

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AI and Machine Learning Network Fetch.ai to connect Autonomous Economic Agents to IOTA Network’s Data Sources

IOTA x Fetch AI

As mentioned in a blog post by Fetch.ai, dated June 10, 2021, for the first time ever, this draws “a clear, substantial path” to addressing the data discoverability problem, and to “connect those that need information to those that have it — seamlessly, autonomously, and in real-time.” As noted in the announcement, today’s world is “drowning” in data, however, most of it is “lost” because those who produce it do not actually share it, and those who require it don’t access it — usually because they “don’t know it’s there, and don’t know where to look for it. Furthermore, much data has a limited shelf-life,” the update from Fetch.ai explained.

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Implementing an End-to-End Machine Learning workflow with Azure Data Factory

Data pipeline

In this article, I will walk through an entire Machine Learning Operation Cycle and show how to establish every step of the way using Azure Data Factory (ADF). Yes, it is possible, easy, and extremely reliable. As a bonus, it also automatically sets you up to receive alerts for any sort of data anomalies occurring throughout the process, so you do not have to worry about monitoring the workflow manually.

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[Paper Xplained] Neural Machine Translation using Bahdanau Attention

The paper in discussion is “Neural Machine Translation by Jointly Learning to Align and Translate” by Dzmitry Bahdanau, KyungHyun Cho & Yoshua Bengio. This is the paper that has introduced the now-famous “Attention Mechanism” in the year 2014. Though there are several advancements that happened to the concept of Attention, the mechanism introduced by this paper is still known as “Bahdanau Attention” or “Additive Attention”

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[Paper] Yoshua Bengio team designs consciousness-inspired Planning Agent for Model-Based RL

Imagine you’re in an airport, searching for your departure gate. Humans have an excellent ability to extract relevant information from unfamiliar environments to guide us toward a specific goal. This practical conscious processing of information, aka consciousness in the first sense (C1), is achieved by focusing on a small subset of relevant variables from an environment — in the airport scenario we would ignore souvenir shops and so on and focus only on gate-number signage — and it enables us to generalize and adapt well to new situations and to learn new skills or concepts from only limited examples.

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[Papers with Code] Spoken Style Learning with Multi-modal Hierarchical Context Encoding for Conversational Text-to-Speech Synthesis

For conversational text-to-speech (TTS) systems, it is vital that the systems can adjust the spoken styles of synthesized speech according to different content and spoken styles in historical conversations. However, the study about learning spoken styles from historical conversations is still in its infancy.

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DouZero: Mastering DouDizhu with Self-Play Deep Reinforcement Learning

Games are abstractions of the real world, where artificial agents learn to compete and cooperate with other agents. While significant achievements have been made in various perfect- and imperfect-information games, DouDizhu (a.k.a. Fighting the Landlord), a three-player card game, is still unsolved. DouDizhu is a very challenging domain with competition, collaboration, imperfect information, large state space, and particularly a massive set of possible actions where the legal actions vary significantly from turn to turn.

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A gentle introduction to Dimensionality Reduction

As the word exploratory suggest, Exploratory Factor Analysis (EFA) is that preliminary examination seeking to understand relationship between variables. When first exposed to the data, the researcher might not hold any assumption about the input variables and their relationship. The researcher starting point is to understand patterns and factors influencing such patterns to identify relationship between variables. EFA, the topic of this article is a statistical technique aiming to identify underlying factors influencing such variables.

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How to make your data project ethical by design

Man holding a light bulb

Data is the lifeblood of companies today. Not only does day-to-day functioning rely on a constant feed of data about every aspect of operations, it’s becoming increasingly clear that with enough data and the right analysis, previously intractable problems can be solved and processes improved. It should come as no surprise that data science is currently ranked #2 on Glassdoor’s 2021 list of best jobs in the US (and has been #1 for 4 of the past 6 years).

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C3.ai Digital Transformation Institute announces AI for Energy and Climate Security Grantees

Climate lock

C3.ai Digital Transformation Institute (C3.ai DTI) announced the second round of C3.ai DTI awards, focused on using artificial intelligence (AI) techniques and digital transformation to advance energy efficiency and lead the way to a lower-carbon, higher-efficiency economy that will ensure energy and climate security.

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10 online Tableau Courses to take in 2021

Data is undoubtedly the backbone of any business organization. A business organization or a market on a whole is unimaginable without data. Needless to mention that, organizations and companies deal with a deluge of data every day. Data is the driver of an organization and thus it is given the highest place. Besides, data organization is imperative to ensure the smooth functioning of an organization.

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