1. Visit the Android Studio Website: Go to the official Android Studio website at https://developer.android.com/studio.

2. Download Android Studio: On the website, you’ll see a big green “Download Android Studio” button. Click on it.

3. Accept Terms and Conditions: You might be prompted to review and accept the terms and conditions of use. Read through them and if you agree, proceed.

4. Download the Installer: Click on the download button for the package you selected. The download should start automatically.

5. Once the download is complete, open the downloaded file.

6. Follow the installation instructions provided by the setup wizard.

7. Once the installation is complete, you can launch Android Studio by double-clicking on the desktop shortcut or searching for it in the Start menu.

That’s it! You’ve successfully downloaded and installed Android Studio on your Windows 10 system. Now you can start building amazing Android applications.

The post How to download android studio in windows 10 appeared first on Artificial Intelligence.

Google wants Artificial Intelligence everywhere. Here are the top Google products are driven by AI

For the past few years, Google has been dominating the field of artificial intelligence. Google’s search engine has revolutionized the internet. From large-scale organizations to kids, Google’s search engine has provided every one of us with easier access to information. The company claims that its advancements in technology and enhanced customer service would not have been possible had it not invested in disruptive technologies like artificial intelligence, machine learning, deep learning, and others.

This article provides a list of the top 10 products manufactured by Google which are powered by artificial intelligence.

Google Search Engine

Artificial intelligence plays a major role in the systematic operation of Google’s search engine. Algorithms make up a crucial part of the search engine. The breakthroughs in deep learning technology have immensely helped the company in improving these algorithms and provide better search results for the users. Without AI, Google would not have been able to make these improvements to filter search patterns and avoid spam.

Google Assistant

Google Assistant is a voice assistant integrated into smartphones and tablets that are powered by Google. This voice assistant can search for anything, from music playlists, restaurants, best beaches, or hotels to multiple contacts on the phone and even call them in the hands-free mode. Google Assistant is also capable of finding locations using Google Maps.

Google Maps

Google Maps uses AI to track the driver’s route and estimate where they are headed and guides them through with no additional commands. It is a handy navigation system that is available on Android, iOS, and the web. Some features offered by this application are satellite imagery, 360-degree maps, indoor maps, and live traffic predictions in the route ahead. Other important features offered by Maps are nearby restaurants, gas stations, places to eat, ATMs, and other points of interest.

Google Translate

Google Translate allows its users to translate one language to another. It uses voice assistants to intercept the spoken language and translate it into the desired language. Users can also use their keypads to type their queries and get instant translations. Google’s advancements with AI and neural machine translations have improved the quality and reliability of these translations.

Google Drive

Google Drive uses the smart scheduling algorithm to assist the users in scheduling meetings based on the existing information about the users’ meetings and habits. The revamped interface of the application would also which files the users will most likely access, and categorize them according to different folders.

Google Ads

Earlier known as AdWords, Google Ads is a part of Google’s Marketing Suite of tools. This application allows businesses and users to advertise their products online. It offers complete control over the management and placement of the ads based on their target audiences. Google uses machine learning algorithms to analyze the profile of the customers’ behaviors and leverage the insights drawn from the data to reach the right individual.

YouTube

YouTube takes the power of artificial intelligence to manage the content on its platform. It automatically removes objectionable content, like racism or terrorism, from the platform. The company’s top priority is to protect its users from harmful content. The company’s AI researchers use trained ML algorithms to create additional effects on the uploaded videos, making the content even more interesting.

Gmail

Users primarily know that Gmail is an e-mail application, but Google has also integrated several AI and ML algorithms to enhance customer experience. One of the many AI integrated features used by Gmail is the smart reply. It analyzes the entire Gmail and proposes a reply, eliminating the need for typing. It uses AI to protect users from spam.

Google Photos

Photos use AI to suggest images and videos that the users can share with their friends and families. This application uses AI to generate cinematic moments to create a moving image from two different shots. Using AI has enabled bringing moments back to life, tapping into nostalgia, and facilitating happy customer experiences.

Google News

Google News application uses machine learning algorithms to automatically sort through headlines from thousands of possible news sites and provide the users with the best and the most preferred news. AI helps understand the people, places, and objects keep track of the developing news story, and presents it before the customers.

The post TOP 10 GOOGLE PRODUCTS EMPOWERED BY ARTIFICIAL INTELLIGENCE appeared first on Artificial Intelligence.

Google makes recommendations in practicing responsible AI.

Technology innovation and development in modern mechanization is making the world brawny, to face the most complex challenges of the upcoming times. In particular, it is Artificial Intelligence that presently is and also in the future will help us in getting the grips on complicated problems. The application of Artificial Intelligence is offering new probabilities for higher productivity augmentation. The swiftness of AI research development is now being matched with real-world applications. How people will take up or accept Artificial Intelligence will decide the influence of AI on the world. In this respect, Google is committed to making progress in the responsible development of AI.

From business, industries, banking to education and healthcare sectors the evolution of AI is generating new opportunities in improving the lives of people all around the world. However, this is also putting up questions about the responsibility in practicing Artificial Intelligence, its accountability, fairness, and also how secure and how privacy is maintained in these systems. To benefit people and society, Google is all in its way to develop Artificial Intelligence responsibly.

As AI is transfiguring the industries and solving important challenges at a large scale, it is a deep learning responsibility to build AI (carrying vast opportunities) that works for everyone.

Google paves the way for value-based AI benefiting businesses.

• For more liable and secured products, Google talks about assessing the AI systems in both forms, when it is performing and also when not performing as it this is important in building accountable products.

• There is lack of trust when it comes to organization selecting product enterprise which is based on Artificial Intelligence and this lack of trust is a growing hurdle. However, a responsible AI outlook earns trust.

• Google takes into consideration for authorizing the AI decision-makers and developers to take ethical rumination into account in finding innovative and new ways to drive AI missions.

According to Google, AI systems should be designed in a way that is well-founded, authentic, efficacious customer-driven following best general practices for software systems along with practices that take into consideration the uniqueness of machine learning.

Google recommended certain practices for responsible AI system-

• Use design featuring a humanistic approach- For a good user’s experience intelligibility and control is pivotal. Therefore, it is required to design the features with suitable divulgence.

• It is crucial to consider proper assistance and incrementation. It means when there is a high assurance that one answer can convince a variety of users then it is appropriate to produce a single answer. In other cases, it might be best for the systems to suggest for few more options to the users. However, it is much difficult to achieve accuracy at one answer compared to precision at a few answers.

• To understand the trade-offs between different kinds of errors and experiences, it is helpful to use several metrics than using a single one.

• It is always required to ensure the metrics which is accurate for the framework and goals of the system. For example, a fire alarm system should have high recall even if it means the occasional false alarm. Also, it is required to include feedback from the surveys received from the users, along with the qualities that will track the overall performance of the system.

• It is required to examine the raw data directly whenever it is possible. Machine Learning models emulate the data on which they are trained. There can be cases where it is not possible to examine the data like with sensitive raw data in such cases it is required to understand the input data as much as possible while respecting confidentiality for instance, by computing aggregate and anonymized summaries.

• To have best test practices it is always best to learn from software engineering and quality engineering so that it is possible to make sure that the AI system is working deliberately and can be trusted.

Google AI principle for responsible AI system-

Google’s AI principles, serve as a living constitution since 2018. Google’s Responsible Innovation team directs to put all the principles to work worldwide. For building a successful AI, continuous evaluations are important. It is required to undertake a deep ethical analysis and also assessments based on risk and opportunity for any product of technology. To look over and understand AI models responsible AI tools are an increasingly operative way.

Google is focusing on building resources like Explainable AI, Model Cards, and the TensorFlow open-source toolkit to provide transparency in the model in an accountable and structured way.

The post GOOGLE MAKES EXHORTATION FOR RESPONSIBLE ARTIFICIAL INTELLIGENCE appeared first on Artificial Intelligence.

In 2013, Google started a pioneering and ostentatious effort to manufacture robots. With time, its target has become self-effacing but with time the technology is also subtly more advanced. Since 2013, the internet company has depleted tens of millions of dollars in buying six robotics start-ups in Japan and in the United States. The project consisted of two teams that are majoring in machines that moved and looked like humans. Andy Rubin, the Vice President of engineering who was behind the effort of creating such projects named it Replicant in a nod to Google’s great ambition. Also, the name was used in a science-fiction movie called ‘Blade Runner’.

However, with the passing time over the next few years, Google sold off the companies that have been acquired by it or shut them down. The Japanese conglomerate SoftBank bought the best-known project named Boston Dynamics from Google.

In recent years, with the advancement of modern technology, Google accumulated and reassessed its target on the mechanics of complex robots. For the last few years, Google has been remodeling its program focusing on robots that are much more manageable and simpler than human-shaped machines.

Vincent Vanhoucke, who previously helped Google in building Google Brain that researches artificial intelligence is now leading the new robotics at Google. This new effort is called robotics at Google. It includes many of the engineers and researchers who worked under Mr. Rubin. Its new model will be able to learn skills independently using machine learning without human intervention, like traversing in a warehouse that is filled with unexpected objects. The machines may not impressive or attractive as the earlier humanoid robots but this more advanced technology incorporated inside them gives them more perspective in the real world.

In the warehouse and on factory floors, Robots are already in use but they can only operate certain specific tasks only like turning screws or picking up objects. Taking the help of machine learning Google wants the machines with which it is working to learn on their own.

The post GOOGLE MADE A COME BACK IN THE WORLD OF ROBOTICS appeared first on Artificial Intelligence.

AI has finally come full circle.

A new suite of algorithms by Google Brain can now design computer chips—those specifically tailored for running AI software—that vastly outperform those designed by human experts. And the system works in just a few hours, dramatically slashing the weeks- or months-long process that normally gums up digital innovation.

At the heart of these robotic chip designers is a type of machine learning called deep reinforcement learning. This family of algorithms, loosely based on the human brain’s workings, has triumphed over its biological neural inspirations in games such as Chess, Go, and nearly the entire Atari catalog.

But game play was just these AI agents’ kindergarten training. More recently, they’ve grown to tackle new drugs for Covid-19, solve one of biology’s grandest challenges, and reveal secrets of the human brain.

In the new study, by crafting the hardware that allows it to run more efficiently, deep reinforcement learning is flexing its muscles in the real world once again. The team cleverly adopted elements of game play into the chip design challenge, resulting in conceptions that were utterly “strange and alien” to human designers, but nevertheless worked beautifully.

It’s not just theory. A number of the AI’s chip design elements were incorporated into Google’s tensor processing unit (TPU), the company’s AI accelerator chip, which was designed to help AI algorithms run more quickly and efficiently.

“That was our vision with this work,” said study author Anna Goldie. “Now that machine learning has become so capable, that’s all thanks to advancements in hardware and systems, can we use AI to design better systems to run the AI algorithms of the future?”

The Science and Art of Chip Design

I don’t generally think about the microchips in my phone, laptop, and a gazillion other devices spread across my home. But they’re the bedrock—the hardware “brain”—that controls these beloved devices.

Often no larger than a fingernail, microchips are exquisite feats of engineering that pack tens of millions of components to optimize computations. In everyday terms, a badly-designed chip means slow loading times and the spinning wheel of death—something no one wants.

The crux of chip design is a process called “floorplanning,” said Dr. Andrew Kahng, at the University of California, San Diego, who was not involved in this study. Similar to arranging your furniture after moving into a new space, chip floorplanning involves shifting the location of different memory and logic components on a chip so as to optimize processing speed and power efficiency.

It’s a horribly difficult task. Each chip contains millions of logic gates, which are used for computation. Scattered alongside these are thousands of memory blocks, called macro blocks, which save data. These two main components are then interlinked through tens of miles of wiring so the chip performs as optimally as possible—in terms of speed, heat generation, and energy consumption.

“Given this staggering complexity, the chip-design process itself is another miracle—in which the efforts of engineers, aided by specialized software tools, keep the complexity in check,” explained Kahng. Often, floorplanning takes weeks or even months of painstaking trial and error by human experts.

Yet even with six decades of study, the process is still a mixture of science and art. “So far, the floorplanning task, in particular, has defied all attempts at automation,” said Kahng. One estimate shows that the number of different configurations for just the placement of “memory” macro blocks is about 10^2,500—magnitudes larger than the number of stars in the universe.

Game Play to the Rescue

Given this complexity, it seems crazy to try automating the process. But Google Brain did just that, with a clever twist.

If you think of macro blocks and other components as chess pieces, then chip design becomes a sort of game, similar to those previously mastered by deep reinforcement learning. The agent’s task is to sequentially place macro blocks, one by one, onto a chip in an optimized manner to win the game. Of course, any naïve AI agent would struggle. As background learning, the team trained their agent with over 10,000 chip floorplans. With that library of knowledge, the agent could then explore various alternatives.

During the design, it worked with a type of “trial-and-error” process that’s similar to how we learn. At any stage of developing the floorplan, the AI agent assesses how it’s doing using a learned strategy, and decides on the most optimal way to move forward—that is, where to place the next component.

“It starts out with a blank canvas, and places each component of the chip, one at a time, onto the canvas. At the very end it gets a score—a reward—based on how well it did,” explained Goldie. The feedback is then used to update the entire artificial neural network, which forms the basis of the AI agent, and get it ready for another go-around.

The score is carefully crafted to follow the constraints of chip design, which aren’t always the same. Each chip is its own game. Some, for example, if deployed in a data center, will need to optimize power consumption. But a chip for self-driving cars should care more about latency so it can rapidly detect any potential dangers.

The Bio-Chip

Using this approach, the team didn’t just find a single chip design solution. Their AI agent was able to adapt and generalize, needing just six extra hours of computation to identify optimized solutions for any specific needs.

“Making our algorithm generalize across these different contexts was a much bigger challenge than just having an algorithm that would work for one specific chip,” said Goldie.

It’s a sort of “one-shot” mode of learning, said Kahng, in that it can produce floorplans “superior to those developed by human experts for existing chips.” A main throughline seemed to be that the AI agent laid down macro blocks in decreasing order of size. But what stood out was just how alien the designs were. The placements were “rounded and organic,” a massive departure from conventional chip designs with angular edges and sharp corners.

Human designers thought “there was no way that this is going to be high quality. They almost didn’t want to evaluate them,” said Goldie.

But the team pushed the project from theory to practice. In January, Google integrated some AI-designed elements into their next-generation AI processors. While specifics are being kept under wraps, the solutions were intriguing enough for millions of copies to be physically manufactured.

The team plans to release its code for the broader community to further optimize—and understand—the machine’s brain for chip design. What seems like magic today could provide insights into even better floorplan designs, extending the gradually-slowing (or dying) Moore’s Law to further bolster our computational hardware. Even tiny improvements in speed or power consumption in computing could make a massive difference.

“We can…expect the semiconductor industry to redouble its interest in replicating the authors’ work, and to pursue a host of similar applications throughout the chip-design process,” said Kahng.

“The level of the impact that [a new generation of chips] can have on the carbon footprint of machine learning, given it’s deployed in all sorts of different data centers, is really valuable. Even one day earlier, it makes a big difference,” said Goldie.

The post A Google AI Designed a Computer Chip as Well as a Human Engineer—But Much Faster appeared first on Artificial Intelligence.

While there is a plethora of innovative artificial intelligence based companies, none come close to Baidu

Baidu Inc., is one of the largest providers of Chinese language Internet services. Today, it is also one of the leading artificial intelligence innovators in the world. The company has helped China position itself on the global tech map while also boosting its economy along with Alibaba and Tencent.

Sources reveal that in 2020 alone, Baidu’s core R&D expenditure accounted for 21.4% of its revenue, becoming one of the top Internet companies with the highest R&D spending. Further, Baidu also claims to have most artificial intelligence-related patent applications in China. This is a testament to Baidu’s long-term commitment to driving technological advancement. Today, Baidu is actively and often successfully integrating artificial intelligence technologies into all of its major businesses. This ranges from search engine, to drug discovery and even autonomous driving. In 2018, Baidu became the first Chinese company to join an artificial intelligence ethics group (Partnership on AI (PAI)) led by top U.S. tech firms, Alphabet Inc’s Google, Apple Inc and Facebook Inc.

Here are some notable innovations in artificial intelligence applications from Baidu:

The COVID-19 Conundrum

During the COVID-19 outbreak too, the company had leveraged its expertise in artificial intelligence, and associated technologies and products, to support frontline efforts to prevent and control the pandemic. It created an artificial intelligence system that uses infrared technology to predict passengers’ temperatures at Beijing’s Qinghe Railway Station. Its Smart Consulting Assistant has also proved resourceful in helping doctors make rapid diagnoses and initiate treatment online.

Last year, Baidu had also open-sourced its Ribonucleic acid (RNA) prediction algorithm LinearFold. This artificial intelligence algorithm aims to accelerate the prediction time of a virus’s RNA secondary structure, which is crucial to understand it and developing vaccines. Researchers found that LinearFold is capable of predicting the secondary structure of the SARS-CoV-2 RNA sequence in only 27 seconds, 120 times faster than other methods. Apart from LinearFold, Baidu has also launched PaddleHelix, a machine learning-based bio-computing framework aimed at facilitating the development of vaccine design, drug discovery, and precision medicine.

Baidu’s Autonomous Vehicles Ventures

 Apollo is an ambitious, open-source platform from Baidu that is designed to support self-driving vehicles. Apollo’s deep-learning inference support is designed to handle complex driving environments, including sensor fusion and AI processing. Baidu’s Automated Valet Parking (AVP) which runs on ACU-Advanced and Xilinx’s hardware is also built on Apollo. Last year, Baidu made headlines for its demonstration of Fully Automated Driving without a safety driver via live streaming. Using Apollo’s new Fully Automated Driving capability, the artificial intelligence system can independently drive without a safety driver inside the vehicle, a breakthrough that will accelerate the large-scale deployment of autonomous driving technology across China.

PaddlePaddle

Baidu’s PaddlePaddle offers software developers of all skill levels the tools, services, and resources they need to rapidly adopt and implement deep learning at scale. It also hosts toolkits for cutting-edge research purposes, like Paddle Quantum for quantum-computing models and Paddle Graph Learning for graph-learning models. Companies like LinkingMed have used PaddlePaddle to develop an AI-powered pneumonia screening and the lesion-detection system being used in the hospital affiliated with Xiangnan University in Hunan Province. By using Paddle Detection, a PaddlePaddle toolkit for image processing, Jinlu Technology trains an instance-segmentation model for sorting waste plastic bottles.

Natural Language Comprehension

One of the most sought after yet trickiest challenges of artificial intelligence algorithms is to enhance its NLP abilities. Baidu’s ERNIE (short for Enhanced Representation through kNowledge Integration), is presently the best in the world by GLUE (General Language Understanding Evaluation) score. ERNIE can understand blocks of language in context and therefore comprehend commands and interactions of all kinds efficiently. Some of the ERNIE’s iterations like ERNIE-GEN enable language generation tasks, like dialogue engagement, question generation, and abstractive summarization. In contrast, ERNIE-ViL helps with visual understanding.

Other Exemplary Inventions

In 2015, Baidu launched its intelligent personal assistant, Duer. Also dubbed as the Chinese Apple Siri, Duer includes multi-modal interaction, natural language processing, and other such technologies for a natural interaction and smarter understanding.

Baidu’s Deep Speech is a state-of-the-art speech recognition system developed using end-to-end deep learning by Baidu Research. It has also developed a production-quality text-to-speech (TTS) system using deep neural network – Deep Voice. Baidu mentions that its Deep Voice is faster and more efficient than Google’s WaveNet. Baidu also has SwiftScribe,  an AI-Powered Transcription Software among its wide array of artificial intelligence innovations. Based on Deep Speech 2, the main function of SwiftScribe is to transcribe audio material into the text in order to solve the problem of consuming a large amount of time-by-word dictation.

Baidu Brain, is another core artificial intelligence innovation from the Beijing based company that features advanced technology for recognizing and processing speech, images and words as well as building user profiles based on big data analysis.

Moreover, last year, Baidu launched its own artificial intelligence-based accelerator called Kunlun K200 SoC. This 256-TOPS accelerator was designed to handle its internal deep-learning workloads. The K200 accelerates common neural-network and SQL operations. On artificial intelligence inference benchmarks, it matches the power efficiency of Nvidia’s T4 card. This year, Baidu will start mass production of Kunlun 2.

The post BAIDU : TOP ARTIFICIAL INTELLIGENCE INNOVATIONS FROM THE CHINESE ‘GOOGLE’ appeared first on Artificial Intelligence.

Google AI has announced the release of Model Search, a platform that will help researchers develop machine learning (ML) models automatically and efficiently. Model Search isn’t domain-specific, flexible, and well equipped to find the appropriate architecture that best fits a given dataset and problem. At the same time, it minimizes the coding time, effort, and resources. Model Search is built on Tensorflow and can run on both distributed settings or a single machine.

The Success of neural networks often depends on the extent to which they can generalize to various tasks. It is challenging to design Neural networks that can generalize well as the research community’s understanding of this concept is limited. The limitations become complicated when Machine Learning domains are taken into consideration. Techniques like neural architecture search (NAS) use algorithms, reinforcement learning (RL), evolutionary algorithms, and combinatorial search to build a neural network from a given search space. Although these techniques can deliver results better than their manually designed counterparts, these algorithms usually compute heavily and need thousands of models to train before converging and are domain-specific.

These shortcomings can be overcome by using Model Search. The Model Search System is built up of multiple trainers, a search algorithm, and a database to store evaluated models. The system can run both training and evaluation experiments in an adaptive yet asynchronous manner. Each trainer conducts experiments on their own, and all the trainers share knowledge from their experiments. At the starting of every cycle, the search algorithm goes over all the completed trials and then uses beam search to determine what to try next. It then implores mutation over one of the best architectures it finds and assigns the resulting model back to a trainer.

The neural network is built from a set of predefined blocks. This approach is more efficient as it explores only structures and not their fundamental and detailed components, thereby reducing the search space scale. As the framework is built on Tensorflow, blocks can implement any function that takes a tensor as an input. Moreover, the blocks provided can be fully defined neural networks that are already known to work for the given problem. In this case, Model Search can be configured to act as a powerful ensembling machine. The search algorithms used in Model Search are adaptive, greedy, and incremental making them converge faster than RL algorithms.

To improve efficiency and accuracy, Model Search enables transfer learning between various internal experiments in two ways: knowledge distillation or weight sharing. Knowledge distillation allows improving candidates’ accuracy by adding a loss term that matches the high-performing models’ predictions in addition to the ground truth. In contrast, Weight sharing bootstraps some of the network’s parameters from previously trained candidates by copying suitable weights from once trained models and randomly initializing the remaining ones.

The researchers claim that Model Search improves upon production models with minimal iterations. They illustrated Model Search’s capabilities in the speech domain by discovering a model for keyword spotting and language identification. It used fewer than 200 iterations and was found to improve efficiency. The researchers also applied Model Search to find an architecture suitable for image classification on the heavily explored CIFAR-10 imaging dataset. They observed that they were quickly able to reach a benchmark accuracy of 91.83 in only 209 trials as compared to 5807 trials for the RL algorithm.

The Model Search Code aims to provide the researchers with a flexible, domain-agnostic framework for ML model discovery. The framework is powerful enough to build models with state-of-the-art performance on well-known problems when provided with a search space composed of standard building blocks. The code extends access to AutoML solutions to the ever-flourishing research community.

The post Google AI Introduces ‘Model Search’: An Open Source Platform For Finding Optimal Machine learning (ML) Models appeared first on Artificial Intelligence.

As dispute over research, diversity grows.

Google fired staff scientist Margaret Mitchell on Saturday, they both said, a move that fanned company divisions on academic freedom and diversity that were on display since its December dismissal of AI ethics researcher Timnit Gebru.

Google said in a statement Mitchell violated the company’s code of conduct and security policies by moving electronic files outside the company.

Mitchell, who announced her firing on Twitter, did not respond to a request for comment.

Google’s ethics in artificial intelligence work has been under scrutiny since the firing of Gebru, a scientist who gained prominence for exposing bias in facial analysis systems.

The dismissal prompted thousands of Google workers to protest.

She and Mitchell had called for greater diversity and inclusion among Google’s research staff and expressed concern that the company was starting to censor papers critical of its products.

Gebru said Google fired her after she questioned an order not to publish a study saying AI that mimics language could hurt marginalised populations.

Mitchell, a co-author of the paper, publicly criticised the company for firing Gebru and undermining the credibility of her work.

The pair for about two years had co-led the ethical AI team, started by Mitchell.

Google AI research director Zoubin Ghahramani and a company lawyer informed Mitchell’s team of her firing on Friday in a meeting called at short notice, according to a person familiar with the matter.

The person said little explanation was given for the dismissal. Google declined to comment.

The company said Mitchell’s firing followed disciplinary recommendations by investigators and a review committee.

It said her violations “included the exfiltration of confidential business-sensitive documents and private data of other employees”. The investigation began Jan. 19.

Google employee Alex Hanna said on Twitter the company was running a “smear campaign” against Mitchell and Gebru, with whom she worked closely. Google declined to comment on Hanna’s remarks.

Google has recruited top scientists with promises of research freedom, but the limits are tested as researchers increasingly write about the negative effects of technology and offer unflattering perspectives on their employer’s products.

Reuters reported exclusively in December that Google introduced a new “sensitive topics” review last year to ensure that papers on topics such as the oil industry and content recommendation systems would not get the company into legal or regulatory trouble. Mitchell publicly expressed concern that the policy could lead to censorship.

Google reiterated to researchers in a memo and meeting on Friday that it was working to improve pre-publication review of papers.

It also announced new policies on Friday to handle sensitive departures and evaluate executives based on team diversity and inclusion.

The post Google fires second AI ethics leader appeared first on Artificial Intelligence.

The Databricks Unified Data Platform, using the Google Kubernetes Engine, can be deployed in a containerized cloud environment and link to Google BigQuery and other GCP services.

Databricks’ Unified Data Platform will be available on the Google Cloud Platform beginning in April, the two companies said Wednesday, completing a trifecta for Databricks whose software already runs on the Amazon Web Services and Microsoft Azure cloud platforms.

Databrick’s software also will be integrated with the Google BigQuery business analytics system and leverage the Google Kubernetes Engine – enabling businesses and organizations to deploy Databricks in a containerized cloud environment for the first time.

With Databricks available on Google Cloud, customers can create “data lakehouses” – a combination of data lake and data warehouse systems that Databricks has been promoting – for a range of business analytics, data engineering, data science and machine learning tasks.

The alliance with Google comes at a time when Databricks has been gaining momentum – and a lot of attention – in the big data space. Earlier this month the company raised $1 billion in a Series G round of financing, boosting its post-money valuation to $28 billion in a move many see as a precursor to an IPO sometime this year.

The alliance also comes as more businesses and organizations are choosing to store their data – especially data managed for analytical tasks – in the cloud and run their business analysis and data visualization tools on cloud platforms.

“The cloud is huge now. But it’s really the beginning of the journey for a lot of customers – especially when it comes to data and data warehouses,” said David Meyer, Databricks senior vice president of product management, in an interview with CRN.

“We’re seeing a very significant acceleration of, specifically, those data workloads moving to the cloud,” said Kevin Ichhpurani, Google corporate vice president and head of Google Cloud global ecosystem and business development, also in the interview with CRN.

Ichhpurani said those efforts to move data and business analytics to the cloud are being driven by rapidly growing data volumes and the role that data analytics, data orchestration and machine learning are playing in digital transformation initiatives and the re-invention of business processes.

The Google executive also emphasized that the alliance goes beyond joint development to include go-to-market and sales activities – including working with channel partners.

The two companies said that members of their partner ecosystems have committed to support Databricks on Google Cloud including systems integrators Accenture, Cognizant and Deloitte; strategic service providers such as Slalom and SADA; and software developer partners including Qlik, Tableau, Informatica and Confluent.

Meyer noted that many businesses and organizations are decommissioning older data warehouse and analytics systems and moving them to the cloud. “It’s a great time for these SIs [systems integrators] to lean in and help these customers figure out where they want to be three or four years from now.”

Ichhpurani said several of the systems integrators who partner with Google and Databricks are interested in launching practices around the Databricks-on-Google Cloud offering.

The Databricks Unified Data Platform on Google Cloud is currently in beta testing and is expected to be generally available in April. It will be available through the Google Cloud Marketplace for simpler procurement, user provisioning, sign-on and unified billing.

Databricks on GCP will allow customers to rapidly deploy and scale Databrick’s software on Google Cloud’s global network and easily adjust the usage rate based on their current needs.

The two companies also said the advanced security and data protection controls provided by the GCP will make the combination especially attractive for use within highly regulated industries.

By using the Google Kubernetes Engine as the operating environment for running Databricks on Google Cloud, Databricks can leverage Kubernetes managed services for security, network policy and compute, according to the two companies. It also speeds the release of new features at scale and at lower cost.

The companies have also developed connectors to integrate Databricks with Google BigQuery, Google Cloud Storage, Google’s Looker data exploration and discovery tool, and Google’s Pub/Sub messaging and data ingestion system.

“By combining Databricks’ capabilities in data engineering and analytics with Google Cloud’s global, secure network—and our expertise in analytics and delivering containerized applications—we can help companies transform their businesses through the power of data,” said Thomas Kurian, Google Cloud CEO, in a statement.

The availability of Databricks on Google Cloud “deliver[s] on our shared vision of a simplified, open and unified data platform that supports all analytics and AI use-cases that will empower our customers to innovate even faster,” said Databricks CEO Ali Ghodsi in the same statement. “This is a pivotal milestone that underscores our commitment to enable customer flexibility and choice with a seamless experience across cloud platforms.”

The post Databricks To Offer Its Big Data Analytics System On Google Cloud appeared first on Artificial Intelligence.

Leveraging Artificial Intelligence to Create Impressive Products for Disabled People

Technology is an excellent way to enhance the lives of people with disabilities. With the advent of artificial intelligence, several avenues of research have opened up that focus on enhancing the lives of people with impairment.

For instance, Facebook has designed an AI tool that can help the blind “see” again. This AI model explains the images on the Facebook feed of a blind person, so the person using the screen reader gets an idea of what is going on in the picture. This means people with visual impairment no longer have to hear a screen reader say “Photo” by “John Doe.” Google’s ‘Look to Speak’ app uses machine learning and computer vision to allow users to control their devices with their eyes

Similarly, OrCam, a Jerusalem-based company, has developed an AI-based called OrCam Read. This handheld device can read full pages or screens of text aloud from any printed or digital surface, including newspapers, books, product labels, and computers and smartphones. Through this device, OrCam aims to help people with reading challenges, such as dyslexia, mild to moderate vision loss, reading fatigue, as well as for those who read large volumes of text.

Even company giants like Microsoft have started a five-year program called ‘AI for Accessibility,’ with an investment of US$25 million, aiming to put AI in the hands of developers to make the world more accessible by providing AI solutions for the specially-abled. Artificial intelligence not only assists people with physical disabilities but is also helping people struggling with learning problems and mental health issues. E.g., Microsoft’s Windows Hello uses biometric login, i.e., fingerprint, face, or iris, which can work for people with physical disabilities or those with dyslexia who might struggle to remember passwords. AI chatbots like Woebot and Wysa are ensuring the availability of consultation for mental health woes, beyond the therapist hours 24/7.

Meanwhile, people suffering from epilepsy can have seizures from blinking lights and animations. This is why accessiBe, a web accessibility platform enables epileptic users to disable various types of animation, such as GIFs and videos so that they can browse the web without complications. Voiceitt is an app for people with speech impediments, including both those who need it temporarily after strokes and brain injuries, and those with more long-term conditions like cerebral palsy, Parkinson’s, and Down’s syndrome. The app uses machine learning to pick up speakers’ unique speech patterns, recognize any mispronunciations, and rectify them before creating an audio or text output. Livio AI, developed by Starkey, an AI medical device company, is a hearing aid that will enhance the hearing experience by quieting all the external noise from the environment and tracking health-related data to enable patients to seek help during emergencies.

Thanks to artificial intelligence, autonomous vehicles also promise to provide people with disabilities more mobility than ever before. Once the self-driving vehicles are fully integrated into society, they can be a resourceful asset for people with different disabilities, including motor impairment. These people would no longer be dependent on other people or public transport.

Further, most of the existing testing methods are highly ineffective at pinpointing learning disabilities like dyslexia or dyscalculia. Artificial Intelligence can help teachers and healthcare professionals diagnose early signs of such conditions and help the students accordingly. For instance, Australian startup Dystech has developed a screening app for early detection of such learning disorders.

Built on Amazon Web Services (AWS), Dystech employs artificial intelligence and machine learning to screen test if the user has dyslexia or dysgraphia. For the former, the app uses datasets of audio recording from both dyslexic and non-dyslexic adults and children to train the AI and relies on users reading aloud words that appear on the screen while being recorded using their smart device during assessment . And for dysgraphia it uses a photo of a handwritten text for screening. After subjected to a 10-minute screening test, app informs users about their likelihood of having dyslexia or dysgraphia.

The post HOW IS ARTIFICIAL INTELLIGENCE TRANSFORMING THE LIVES OF PEOPLE WITH DISABILITIES? appeared first on Artificial Intelligence.