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GoogleIBM CorporationMicrosoft CorporationAmazon Web ServicesBigMLFICOYottamine AnalyticsErsatz LabsPredictron LabsH2O.aiAT&TSift Science

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Market Segment by Regions, regional analysis covers

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Machine Learning as a Service Market By Type:

Software ToolsCloud and Web-based Application Programming Interface (APIs)Other

Machine Learning as a Service Market By Application:

ManufacturingRetailHealthcare & Life SciencesTelecomBFSIOther (Energy & Utilities, Education, Government)

The research provides answers to the following key questions:

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Table of Contents:

Machine Learning as a Service

In conclusion, the Machine Learning as a Service Market report is a reliable source for accessing the Market data that will exponentially accelerate your business. Besides, the report presents a new task SWOT analysis, speculation attainability investigation, and venture return investigation.

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Global Coronavirus Impact And Implications On Global Machine Learning as a Service Market Analysis, Growth, Trends, Share and Forecast to 2026 -...

Daphne Koller is best known as the cofounder of Coursera, the open database for online learning that launched in 2012. But before her work on Coursera, she was doing something much different. In 2000, Koller started working on applying machine learning to biomedical datasets to understand gene activity across cancer types. She put that work on hold to nurture Coursera, which took many more years than she initially thought it would. She didnt return to biology until 2016 when she joined Alphabets life science research and development arm Calico.

Daphne Koller [Photo: couretsy of Insitro]Two years later, Koller started Insitro, a drug discovery and development company that combines biology with machine learning. Im actually coming back to this space, she says.

Theres a lot of hope that artificial intelligence could help speed up the time it takes to make a drug and also increase the rate of success. Several startups have emerged to capitalize on this opportunity. But Insitro is a bit different from some of these other companies, which rely more heavily on machine learning than biology.

By contrast, Insitro has taken the time to build a cutting-edge laboratory, an expensive and time consuming project. Still, having equal competency in lab based science and computer science may prove to be the winning ticket. Though only two years old, Insitro has already caught the attention of old-guard pharmaceutical companies. Last year, the company struck a deal with pharmaceutical giant Gilead to develop tools and hopefully new drug targets to help stop the progression of non-alcoholic fatty liver disease (NASH). The partnership netted Insitro $15 million with the potential to earn up to $200 million.

I spokewith Koller to discuss what her company is doing differently and where machine learning may ultimately make a difference in drug development and discovery. This interview has been edited for publication.

Fast Company: What youre doing is different than most artificial intelligence drug companies, which are using the existing knowledge base of articles and published studiesto come up with drug targets. Instead, youve developed a drug company that uses artificial intelligence but also has a full lab for biologists. Why did you take this approach?

Daphne Koller: The other model is a much easier startup effort in the sense that theres all this data out there and you can go and collect it. You can do it with a team of purely data-science folks. You dont need to build up a wet lab, you just go and collect all those data and you put them in a big pile and then you let your machine learning people have at it.

What were doing is much more complicated and ambitious on a number of different dimensions. One is that we really did need to build up a high-throughput biology lab, which is beyond the frontier on multiple levels. That requires a much more expensive build. It also requires building up a team thats really not been built before, which is taking some people who are at the cutting edge of their field, on the biology side, and putting them together in a single integrated team with some people who are at the cutting edge of machine learning and data science, and really telling them, you speak different languages, but youre going to work together as a single team. And I think thats really a very challenging cultural effort that most companies havent been willing or able to pursue.

FC: Why do that? Whats the benefit of having a drug company that gives biologists and data scientists and machine learning experts equal standing?

DK: When you look at the drug discovery processwhich, if youre lucky, is 15 years end-to-end with a 5% chance of successthere are multiple forks in the road where currently people are making decisions. Do I go down path A or B or C or D? And if youre lucky, one path in 99 will lead you to success. If you go down the wrong one, then its years and tens of millions of dollars in wasted spend. So what if we could make better predictions on which fork to take?

Part of the problem biopharma has had is that its really difficult to fail fast.

What we hope to be able to do, because were building these predictive models, is to be able to make the decisions faster.

The other piece is that machine learning has become pretty good at making accurate predictions across a broad spectrum of domains. Its not been as effectively applied so far in life sciences broadly, and one of the main reasons for that is just the lack of high-quality data that we have [compared to] computer vision or natural language processing or logistics. At the same time, the bioengineering cell biology community has invented in the last few years a remarkable suite of tools that can really be put together in unique and interesting ways to generate massive amounts of data that can help feed those machine-learning algorithms.

If you put those two together, the high throughput biology piece and the machine learning piece, perhaps that provides a way in which we could build these predictive models that make better predictions in pharma research and development.

FC: What is the biggest reasons that drugs fail?

DK: We know from the statistics that most drugs [that go into trials] fail because of lack of efficacy in phase two or phase three. And its not because the drug wasnt good. It was targeting the wrong target. Where the machine learning comes in is to look holistically at many, many different attributes of those cells and say which of them are the most predictive of human clinical outcome. And that is something that people are really not that good at, because cells are complex and theres many dimensions toputting all those pieces together to detect, what often times is a subtle signal. Its not something that people excel in.

FC: So once you set up these apps, how can you use them?

DK: You can use those apps in a variety of ways. First of all, you could use them to identify targets by basically saying, Hey, now we know what a sick cell looks like. Now we know what a healthy cell looks like. What if I [use] CRISPR to perturb the cell to move from an active to an inactive state or vice versa? Well, if you do that, and the phenotype goes from an unhealthy to a healthy state, maybe that gene is a good target for a drug.

People think that Alzheimers is one diseasealmost certainly, thats not true.

People think that Alzheimers is one diseasealmost certainly, thats not true. People think that type two Diabetes is one diseasealso probably not true. For these diseases, we havent yet identified subtypes. We believe that by collecting enough data on enough different genetics at the molecular level, maybe those subtypes will emerge.

FC:Do you have any insight around the role that machine learning can play in helping come up with either a treatment or a vaccine for COVID-19?

DK: I think that there are opportunities. Right now, were looking at vaccine approaches that different companies have developed, and were putting them in with a bunch of viral protein and hoping for the best. To predict vaccine efficacythe techniques just dont exist, and theres not going to be enough time to develop them. But I do think that theres some interesting work thats happening on the therapeutic side, where theres been more work on the application of machine learning to everything from the interpretation of cellular [gene expression]. There is potential for designing new drugs, new drug combinations, and even just interpretation of the cellular state.

FC: Youre working with Gilead on better understanding non-alcoholic fatty liver disease (NASH). Whats difficult about NASH is that it can only be diagnosed and monitored through liver biopsy, which is brutal for the patient. Youve said that youve had some success with machine learning apps being able to detect aspects of the disease that a human cannot otherwise detect, which holds a lot of promise for changing even just the way doctors track the disease in individuals. Im curious what are other areas of human health are interesting to you?

DK: We feel like neuroscience is an area thats about to burst wide open in finally understanding the very complex genetics of Central Nervous System diseases. The unmet need is huge, and the animal models are particularly untranslatable. So for some diseases you could say, Well, the animal model is not great, but its acceptable. The animal model for depressionand this is going to sound surreal, but Im telling you, its not its to take a mouse and you put in a bucket with water and you make it swim until it gets really tired and drowns. And if its swims longer, its less depressed.Its called the forced swim test.

Now, the thing is, if you look at depression, it is a disease with significant genetic heritability where we know that theres hundreds of genes that are implicated with very specific pathways, and stuff that is all now starting to emerge from the genetics and single cell analysis of brain tissue. None of that has anything to do with making a mouse swim longer. We think that in things like neuro-degeneration and neuropsychiatry theres a tremendous opportunity for a different set of tools to be applied. I guarantee you, they will not be perfect models of the disease. But they cant be that much worse than making a mouse swim longer. Right?

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Insitro' s Daphne Koller on AI and drug discovery - Fast Company

Adoption of artificial intelligence (AI) is growing worldwide, according to an IDC survey of more than 2,000 IT and line of business (LoB) decision makers.

Over a quarter of all AI initiatives are already in production and more than one third are in advanced development stages. And organizations are reporting an increase in their AI spending this year.

Delivering a better customer experience was identified as the leading driver for AI adoption by more than half the large companies surveyed. At the same time, a similar number of respondents indicated that AIs greatest impact is in helping employees to get better at their jobs.

Whether it is an improved customer experience or better employee experience, there is a direct correlation between AI adoption and superior business outcomes.

Early adopters report an improvement of almost 25 percent in customer experience, accelerated rates of innovation, higher competitiveness, higher margins, and better employee experience with the roll out of AI solutions.

Organizations worldwide are adopting AI in their business transformation journey, not just because they can but because they must to be agile, resilient, innovative, and able to scale, said Ritu Jyoti, program vice president, Artificial Intelligence Strategies.

While there is considerable agreement on the benefits of AI, there is some divergence in how companies deploy AI solutions. IT automation, intelligent task/process automation, automated threat analysis and investigation, supply and logistics, automated customer service agents, and automated human resources are the top use cases where AI is being currently employed.

While automated customer services agents and automated human resources are a priority for larger companies (5000+ employees), IT automation is the priority for smaller and medium sized companies (less than 1000 employees).

Despite the benefits, deploying AI continues to present challenges, particularly with regard to data. Lack of adequate volumes and quality of training data remains a significant development challenge. Data security, governance, performance, and latency (transfer rate) are the top data integration challenges.

Solution price, performance and scale are the top data management issues. And enterprises report cost of the solution to be the number one challenge for implementing AI. As enterprises scale up their efforts, fragmented pricing across different services and pay-as-you-go pricing may present barriers to AI adoption.

An AI-ready data architecture, MLOps, and trustworthy AI are critical for realizing AI and Machine Learning at scale, added Jyoti.

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There is a direct correlation between AI adoption and superior business outcomes - Help Net Security

DUBLIN--(BUSINESS WIRE)--The "Geospatial Analytics Artificial Intelligence Market - Forecast (2020 - 2025)" report has been added to ResearchAndMarkets.com's offering.

The global geospatial analytics AI market is estimated to grow at a CAGR of 24.00% during the forecast period 2020-2025.

APAC is projected to be the fastest-growing market with a CAGR of 32.6%, which can be mainly attributed to the growing usage of geospatial analytics across various booming industrial sectors as well as the growing infrastructural developments in the region.

Geospatial Analytics AI Market Outlook

Geospatial Analytics Artificial Intelligence employs Artificial Intelligence technologies such as deep learning and machine learning to combine geographic information system (GIS) with business intelligence information. Geospatial Analytics AI finds a wide range of applications in logistics, transportation, surveying, agriculture, sales and marketing, medical and others. The multidisciplinary applications of Geospatial Analytics AI is primarily due to the assistance of the technology in decision making, resource planning and allocation that are essential in many industries. According to Industry ARC findings, the hazard assessment application segment will hold the largest market share during the forecast period.

Geospatial Analytics AI Market Growth Drivers

The growth of the transportation industry is expected to drive the geospatial analytics AI market during the forecast period. The aviation industry is a major segment where technology can be helpful in streamlining operations by controlling air traffic and increasing safety and security in airports. The growing fraudulent activities in the Banking, Financial Services, and Insurance (BFSI) sector are also expected to drive the market for Geospatial Analytics AI Market.

Geospatial Analytics AI Market Challenges

High costs of technology is a major challenge that is hampering the adoption of technology despite a wide number of applications across various industry verticals. Additionally, there are many national laws and regulations imposed by various countries that curtail the growth of the industry.

Geospatial Analytics AI Market Research Scope

The base year of the study is 2020, with forecast done up to 2025. The study presents a thorough analysis of the competitive landscape, taking into account the market shares of the leading companies. It also provides information on unit shipments. These provide the key market participants with the necessary business intelligence and help them understand the future of the Geospatial Analytics AI Market. The assessment includes the forecast, an overview of the competitive structure, the market shares of the competitors, as well as the market trends, market demands, market drivers, market challenges, and product analysis. The market drivers and restraints have been assessed to fathom their impact over the forecast period. This report further identifies the key opportunities for growth while also detailing the key challenges and possible threats.

Geospatial Analytics AI Market Report: Industry Coverage

Key Topics Covered:

1. Geospatial Analytics Artificial Intelligence Market - Overview

2. Geospatial Analytics Artificial Intelligence Market - Executive summary

3. Geospatial Analytics Artificial Intelligence Market

3.1. Comparative analysis

3.1.1. Product Benchmarking - Top 10 companies

3.1.2. Top 5 Financials Analysis

3.1.3. Market Value split by Top 10 companies

3.1.4. Patent Analysis - Top 10 companies

3.1.5. Pricing Analysis

4. Geospatial Analytics Artificial Intelligence Market Forces

4.1. Drivers

4.2. Constraints

4.3. Challenges

4.4. Porters five force model

5. Geospatial Analytics Artificial Intelligence Market -Strategic analysis

5.1. Value chain analysis

5.2. Opportunities analysis

5.3. Product life cycle

5.4. Suppliers and distributors Market Share

6. Geospatial Analytics Artificial Intelligence Market - By Data Source (Market Size -$Million / $Billion)

6.1. Market Size and Market Share Analysis

6.2. Application Revenue and Trend Research

6.3. Product Segment Analysis

7. Geospatial Analytics Artificial Intelligence Market - By Solution (Market Size -$Million / $Billion)

7.1. Hardware

7.1.1. Memory

7.1.2. Processor

7.1.3. Others

7.2. Software

7.2.1. By Deployment

7.2.1.1. Cloud

7.2.1.2. On-Premise

7.3. Services

8. Geospatial Analytics Artificial Intelligence Market - By Machine Learning (Market Size -$Million / $Billion)

8.1. Unsupervised Learning

8.2. Supervised Learning

8.3. Reinforced Learning

8.4. Semi-supervised Learning

8.5. Deep Learning

8.6. Others

9. Geospatial Analytics Artificial Intelligence Market - By Applications (Market Size -$Million / $Billion)

9.1. Geographic Information

9.2. Real Estate

9.3. Coastal application

9.4. Sales & Marketing

9.5. Fraud Detection

9.6. Transport & Logistics

9.7. Agriculture

9.8. Surveying

9.9. Hazard assessment

9.10. Natural Resource Management

9.9. Others

10. Geospatial Analytics Artificial Intelligence - By Geography (Market Size -$Million / $Billion)

10.1. Geospatial Analytics Artificial Intelligence Market - North America Segment Research

10.2. North America Market Research (Million / $Billion)

10.3. Geospatial Analytics Artificial Intelligence - South America Segment Research

10.4. South America Market Research (Market Size -$Million / $Billion)

10.5. Geospatial Analytics Artificial Intelligence - Europe Segment Research

10.6. Europe Market Research (Market Size -$Million / $Billion)

10.7. Geospatial Analytics Artificial Intelligence - APAC Segment Research

10.8. APAC Market Research (Market Size -$Million / $Billion)

11. Geospatial Analytics Artificial Intelligence Market - Entropy

11.1. New product launches

11.2. M&A's, collaborations, JVs and partnerships

12. Geospatial Analytics Artificial Intelligence Market - Industry / Segment Competition landscape Premium

12.1. Market Share Analysis

12.1.1. Market Share by Country- Top companies

12.1.2. Market Share by Region- Top 10 companies

12.1.3. Market Share by type of Application - Top 10 companies

12.1.4. Market Share by type of Product / Product category- Top 10 companies

12.1.5. Market Share at global level- Top 10 companies

12.1.6. Best Practises for companies

13. Geospatial Analytics Artificial Intelligence Market Company Analysis

13.1. Market Share, Company Revenue, Products, M&A, Developments

13.2. Google

13.3. Microsoft

13.4. Geoblink

13.5. ESRI

13.6. Trimble Inc.

13.7. HEXAGON

13.8. Harris Corporation

13.9. Digital Globe

13.10. Bentley Systems

13.11. Incorporated

13.12. General Electric

14. Geospatial Analytics Artificial Intelligence Market - Appendix

For more information about this report visit https://www.researchandmarkets.com/r/isah13

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Geospatial Analytics Artificial Intelligence Market (2020 to 2025) - Drivers, Constraints and Challenges - ResearchAndMarkets.com - Business Wire

London, June 15, 2020 (GLOBE NEWSWIRE) -- The automotive artificial intelligence market is expected to grow at a CAGR of 39.8% from 2019 to reach $15.9 billion by 2027.

Several established automotive organizations across the globe are increasingly struggling with the rising cost of operations, dissatisfied customers, declining sales, and unidentified competition. Advanced capabilities of AI, coupled with rising consumer expectations, have pushed the automotive industry into adopting artificial intelligence. Several organizations are investing heavily in order to reap the profits in highly dynamic and competitive market environments. The global artificial intelligence in automotive market is expected to witness strong growth over the coming years due to the growing demand for autonomous vehicles, adoption of advanced automotive solutions, growing adoption of artificial intelligence for traffic management, and government initiatives and investments towards connected and autonomous vehicles.

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The increasing volume of data gathered through IoT devices, coupled with the widespread availability of high-speed broadband networks and the emergence of 5G technologies is driving the need for faster data processing. Apart from this, widening the implementation of computer vision technologies across vehicles and shifting consumer preferences for premium vehicles to improve the driving experience while enhancing the vehicle and pedestrian safety are some of the key factors anticipated to drive the growth of artificial intelligence in automotive market in the near future. However, lack of infrastructure coupled with the high procurement operating cost is expected to challenge the growth of the artificial intelligence in automotive market growth during the forecast period.

The global market for artificial intelligence in automotive industry is expected to grow at a CAGR of 39.8% from 2019 to reach $15.9 billion by 2027. The market is witnessing consistent growth owing to the increasing demand for smart IoT devices in automotive, surging demand for connected vehicles, and adoption of advanced driver assistance systems. Apart from this, surging adoption of AI-based solutions and services among the automotive industry is also contributing to the overall growth of artificial intelligence in automotive market. While developed economies offer technological growth opportunities through the proliferation of advanced technologies, the ongoing digital transformation initiatives across emerging economies such as Asia-Pacific and Latin America are likely to offer high growth opportunities for vendors operating in the market.

The global artificial intelligence in automotive market is mainly segmented by components (hardware, software, services), by technology (machine learning, computer vision, natural language processing, context-aware computing), by process (signal recognition, image recognition, voice recognition, data mining), by application (semi-autonomous driving, human-machine interface), and region.

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Based on components, the artificial intelligence in automotive market is segmented into hardware, software, and services. The software segment dominated the artificial intelligence in automotive market in 2019 in terms of market share. This is mainly attributed to the growing usage of learning analytics, growing acceptance of in-car assistants driven by machine learning techniques and an increase in demand for autonomous platforms for automotive industry. However, the services segment is slated to grow at the fastest CAGR during the forecast period and will emerge as the major segment in terms of market share by 2027. This growth is mainly driven by the surging demand for AI-based cloud services for autonomous vehicles, over-the-air (OTA) software services, traffic and mapping services, shared mobility services, remote maintenance services, technical support & training services, maintenance & support services, integration services, performance measurement services, and consulting services.

Based on technology, the artificial intelligence in automotive market is segmented into machine learning, computer vision, natural language processing, and context-aware computing. The machine learning technology segment held the largest share of the overall automotive artificial intelligence market in 2019, owing to the demand for signal diagnosing, image recognition, speech recognition, data mining, and an increase in unstructured data generated by the automotive industry. However, the computer vision technology is slated to grow at the fastest CAGR during the forecast period, due to a widening implementation of computer vision in semi-autonomous vehicles to tackle distracted/ drowsy driving and surging use of LIDAR sensors and cameras to avoid vehicle collisions.

Based on process, the overall artificial intelligence in automotive market is segmented into signal recognition, image recognition, voice recognition, and data mining. The signal recognition segment dominated the artificial intelligence in the automotive market in 2019 and is also estimated to continue its dominance over the forecast period. The growth in this market segment is attributed to the increasing growth of automotive safety systems, rising consumer preference for signal recognition in autonomous vehicles, and government regulations pertaining to the safety rating of a vehicle to reduce road collisions. However, the image recognition process is slated to grow at the fastest CAGR during the forecast period, due to growing demand for advanced driver assistance systems (ADAS) such as road signs detection and pedestrian protection systems.

Based on application, the artificial intelligence in automotive market is majorly segmented into semi-autonomous driving and human-machine interface. The human-machine interface segment dominated the artificial intelligence in automotive market in 2019. This is attributed to the increasing demand for interactive technologies in vehicles, connected systems, and smart convenient features.

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Geographically, the global artificial intelligence in automotive market is segmented into five regions, namely, North America, Europe, Asia-Pacific, Latin America, and the Middle East & Africa with a further analysis of major countries in these regions. North America accounted for the largest share of global artificial intelligence in automotive market in 2019, followed by Europe and the Asia-Pacific region. The largest share of this region is mainly attributed to the presence of developed economies, such as the United States and Canada, focusing on enhancing the existing solutions in the automotive industry, and the existence of major players in this market along with a high willingness to adopt advanced technologies. Apart from this, the growing demand for enhanced user experience, rising living standards, growing adoption of autonomous vehicles and availability of high-end infrastructure, increasing R&D expenditure, and various government initiatives supporting AI research are contributing to the growth in this region.

On the other hand, Asia-Pacific region is projected to grow at the highest CAGR during the forecast period. This growth is attributed to an increase in demand for premium vehicles, growing investments in AI technology for improved productivity, and increasing adoption of AI-based solutions and services in the automotive industry. Apart from this, developing internet & connectivity infrastructure, growing adoption of intelligent solutions and increasing digitalization, and increasing investments by the major players in this region are contributing to the growth in the Asia Pacific AI in automotive market.

Some of the key players operating in the global artificial intelligence in automotive market are Google LLC (U.S.), IBM Corporation (U.S.), Intel Corporation (U.S.), Microsoft Corporation (U.S.), Nvidia Corporation (U.S.), Tesla, Inc. (U.S.), Xilinx, Inc. (U.S.), Micron Technology, Inc. (U.S.), Ford Motor Company (U.S.), General Motors Company (U.S.), Harman International Industries Inc. (South Korea), Honda Motor Co., Ltd. (Japan), Audi AG (Germany), and Qualcomm Technologies, Inc. (U.S.), among others.

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Scope of the Report:

Automotive AI Market, by Component

Automotive AI Market, by Technology

Automotive AI Market, by Process

Automotive AI Market, by Application

Automotive AI Market, by Geography

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Automotive Artificial Intelligence Market Worth $15.9 Billion by 2027, Growing at a CAGR of 39.8% from 2019- Global Market Opportunity Analysis and...

The latest report on Data Science and Machine Learning Service Industry market now available at MarketStudyReport.com, delivers facts and numbers regarding the market size, geographical landscape and profit forecast of the Data Science and Machine Learning Service Industry market. In addition, the report focuses on major obstacles and the latest growth plans adopted by leading companies in this business.

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Data Science and Machine Learning Service Market Size 2020 Application, Trends, Growth, Opportunities and Worldwide Forecast to 2025 - 3rd Watch News

Researchers from The University of Tokyo create a new integrated 3D-circuit architecture for AI applications with spiraling stacks of memory modules, which may help lead to specialized machine-learning hardware that uses much less electricity. Credit: Institute of Industrial Science, The University of Tokyo

Researchers from the Institute of Industrial Science at The University of Tokyo designed and built specialized computer hardware consisting of stacks of memory modules arranged in a 3D-spiral for artificial intelligence (AI) applications. This research may open the way for the next generation of energy-efficient AI devices.

Machine learning is a type of AI that allows computers to be trained by example data to make predictions for new instances. For example, a smart speaker algorithm like Alexa can learn to understand your voice commands, so it can understand you even when you ask for something for the first time. However, AI tends to require a great deal of electrical energy to train, which raises concerns about adding to climate change.

Now, scientists from the Institute of Industrial Science at The University of Tokyo have developed a novel design for stacking resistive random-access memory modules with oxide semiconductor (IGZO) access transistor in a three-dimensional spiral. Having on-chip nonvolatile memory placed close to the processors makes the machine learning training process much faster and more energy-efficient. This is because electrical signals have a much shorter distance to travel compared with conventional computer hardware. Stacking multiple layers of circuits is a natural step, since training the algorithm often requires many operations to be run in parallel at the same time.

For these applications, each layers output is typically connected to the next layers input. Our architecture greatly reduces the need for interconnecting wiring, says first author Jixuan Wu.

The team was able to make the device even more energy efficient by implementing a system of binarized neural networks. Instead of allowing the parameters to be any number, they are restricted to be either +1 or -1. This both greatly simplifies the hardware used, as well as compressing the amount of data that must be stored. They tested the device using a common task in AI, interpreting a database of handwritten digits. The scientists showed that increasing the size of each circuit layer could enhance the accuracy of the algorithm, up to a maximum of around 90%.

In order to keep energy consumption low as AI becomes increasingly integrated into daily life, we need more specialized hardware to handle these tasks efficiently, explains Senior author Masaharu Kobayashi.

This work is an important step towards the internet of things, in which many small AI-enabled appliances communicate as part of an integrated smart-home.

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New Integrated 3D-Circuit Architecture With Spiraling Memory for More Efficient AI - SciTechDaily