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Cognitive Capabilities for Aero-engines' Aftermarket

Partners: Pratt & Whitney, McKinsey & Company Canada, MOOV AI, Cognitive Group, Standard Aero Limited Winnipeg, Vector Aerospace Engine Services- Atlantic Inc, Schaeffler, Paradigm Orillia Technology, Meloche

SCALE AI's investment: $7.3 million

Total investment: $23.5 million

Pratt & Whitney Canada supports the broadest, most diverse fleet of aircraft engines in its industry, with more than 13,000 operators and nearly 65,000 engines in service. Customer trends that require increasingly faster global logistics coupled with complex supply chains and demand variability make AI a natural next step to deliver speed and availability.

"Artificial Intelligence is a powerful tool that cuts through complex data to provide predictive insights, resulting in a strong, reliable demand planning that extends all the way through our supply chain. As our industry recovers from the impact of COVID-19, accelerating supply chain efficiency to ensure our customers have the parts and services they need to grow their businesses is more important than ever," remarked Maria Della Posta, president of Pratt & Whitney Canada. "We're excited about developing our AI capabilities, which, combined with decades of innovation, will position us with an enduring competitive advantage throughout every aspect of our business."

Predictive Analytics for Aviation

Partners: Bombardier, Globvision, Ivado Labs

SCALE AI's investment: $5.8 million

Total investment: $11.7 million

Aircraft maintenance is typically composed of largely scheduled activities at specific intervals and unscheduled activities when aircraft parts need repair. Bombardier's Aftermarket team aims to transform its core approach and culture from traditional maintenance to condition-based monitoring and preventive maintenance. The goal of the project is to enable a more proactive approach to maintenance, limit aircraft downtime and reduce "Aircraft on Ground" (AOG) costs for Bombardier aircraft, as well as providing unique state-of-the-art maintenance support to customers.

"As part of its digital transformation, Bombardier is seeking to improve its supply chain and aircraft support activities by developing predictive analytics models. The predictions, which will be powered by advanced AI algorithms, are expected to reduce maintenance costs and aircraft downtime, as well as considerably improve the overall customer experience" says Jean-Christophe Gallagher, Executive Vice President, Services and Support, and Corporate Strategy, Bombardier.

AI Based Quality Control and Machine Analytics

Partners: Crosswing inc, Shield Medical Products, The Design Quantum, York University, Big Nano, LifeCycle Revive, Performance BioFilaments, Kimberly-Clark, Innomar Strategies, Cardinal Health

SCALE AI's investment: $1.3 million

Total investment: $4.2 million

The project will deliver a comprehensive quality control system that will leverage both AI and machine learning through computer vision to increase quality standards and production outputs. This system will also integrate IoT and sensor data into a machine analytics module that will compile and analyze telemetry to enable predictive maintenance. "By significantly improving the quality control standards while meeting demand and reducing the need for labor intensive processes, this will create an increase in customer trust, production capacity, and shared intelligence for SMP and its supply chain partners.", mentions Aryan Durrani, Managing Partner.

The transformative benefits of the technology in this project can be realized across many broader adjacent markets such as clothing and accessories manufacturing. By design, this solution will be deployable quickly without the need for specialized resources such as data scientists and machine vision engineers.

AI-powered Satellite Constellation Production

Partners: MDA, Connektica Solutions, Mafina Solutions, Ivado Labs

SCALE AI's investment: $2.7 million

Total investment: $5.7 million

"Today's satellite market is rapidly pivoting from launching a small number of largely independent satellites to deploying hundreds of satellites in constellations working together as a single system. This transformational shift adds a level of complexity and innovation that requires satellite manufacturers and their supply chain partners to rethink how we design and build our satellite systems. As a result, the supply chain management required to support the scale and complexity of constellation projects is becoming exponentially more technology intensive," explains Mike Greenley, CEO, MDA.

In response to this challenge, MDA is seeking to transform its supply chain operations by leveraging artificial intelligence (AI) to support the volume production of its space products.

The solution will optimize supply chain inventory, manufacturing schedules and performance during the production of the satellites and their subsystems.

AloT fulfullment

Partners: Attabotics, Canadian Tire, Microsoft, Intel technology of Canada ULC, AltaML and Amii

SCALE AI's investment: $2.5 million

Total investment: $7 million

Attabotics has developed an innovative 3D robotic goods-to-person storage system that offers automated retrieval and real-time order fulfillment. By reducing a company's warehouse needs by 85% and reducing labour costs by 75%, Attabotics' solution is enhancing logistics and inventory management across Canada and beyond. Adding AI (Machine learning & Operations Research) to the command and control of the robotic solution makes the Attabotics offering more efficient while maximizing the throughput and uptime.

"These advancements will have a direct impact on the serviceability of the product. Not only will customers see lower operating costs, which are already significantly decreased by our technology, but by maximizing the number of items picked-up by the robot, orders will be gathered faster and more efficiently, further reducing the fulfillment cost for our customers" mentions Neeraj Gupta, Chief Strategy Officer at Attabotics.

AIStotle

Partners: SemiosBIO Technologies Inc., Laughing Coyote Orchards, Ivado Labs

SCALE AI's investment: $1.6 million

Total investment: $3.6 million

As the leading precision-farming platform, and the world's largest independant agtech provider, Semios is focused on simplifying the grower's experience by bringing important, interrelated crop data into one easy-to-use platform. Leveraging a network of sensors that provide more than 500M data points measuring climate, soil moisture, insect and disease activity daily, Semios applies big data analytics and machine learning to reduce and mitigate crop risks for growers. With weather being the primary driver of nearly all crop and pest development, granular, accurate, and reliable forecasting that is hyper-localized to an orchard's unique conditions is imperative.

"This project will leverage Semios' robust historical data set of in-canopy microclimate conditions to develop spatial weather forecasting tools designed to account for variability in a site's crop and topographic characteristics. These new capabilities will provide growers with more precise and granular weather predictions that are tailored to the in-canopy conditions of their orchard, supporting more reliable decision-making. Through these activities, we will be able to identify key geospatial locations for sensor placement that will provide the opportunity to help growers worldwide reduce chemical inputs, better manage water, organize farming data and improve crop outcomes", mentions Stuart Shiell, Lead, Data Insights at SemoisBIO.

AI for Energy Supply Chain Feedstock Optimization

Partners: Tidewater, Validere, Nova Analytics, Queen's University

SCALE AI's investment: $1.2 million

Total investment: $3.6 million

Trillions of dollars of commodities change hands multiple times in the Canadian and global energy supply chains with little real-time visibility into their attributes. The resulting inefficiencies increase energy costs and emissions across the world's largest supply chain. Tidewater has partnered with Validere to use an AI-powered, universal data layer that consolidates and augments multiple data sources into a single source of truth to make better operational, commercial, and ESG decisions. The best course of action can now be determined at a system level by sourcing the appropriate feedstocks for all steps of the supply chain. The result is better economics, reduced energy costs for Canadians, and tangible greenhouse gas emissions reductions.

"We are proud to be involved in creating technology jobs in Canada and this project is a great example of the large role energy plays in all facets of the Canadian economy. These tools are useful in both day-to-day operations, but also in the strategic planning of product movements." - Terrence Dumont, EVP Montney at Tidewater Midstream.

"Validere is excited to continue our work with great partners like Tidewater and SCALE AI. Making the energy supply chain more efficient using technology is one of the biggest environmental and economic opportunities available today. We are especially proud to be creating our technology in Canada and driving job growth here. Validere has created over 20 Canadian jobs this year, and this project has certainly supported our growth." - Nouman Ahmad, co-founder and CEO at Validere

Staff optimizer and two-way product recommender for eCommerce

Partners: Kognitive Tech Inc., Ivado Labs, Roy Foss, Cellcom

SCALE AI's investment: $1 million

Total investment: $2.9 million

Kognitive Tech, a leading eCommerce enterprise, is excited to be adding their first AI capabilities to their software suite. This project is designed to build strong personalization capabilities scalable for retailers. The developed tools will first provide retailers their clients' clustering, scoring, and prioritization in real-time and with machine learning in order to better capture demand and optimize their store resources (e.g., required staff at a given time). A product recommender will subsequently provide product and service recommendations for the relevant customers' clusters based on expected customer needs and available inventory.

"The goal with this project is to ultimately grow retailer's transaction conversion rates, or demand capture, while increasing supply chain efficiency. It will be very beneficial to retailers as they will be able to offer more tailored products and services to their customers", specifies Josh Singer, CEO.

Industrial video analytics for manufacturing efficiency Partners: Pacefactory inc, Magna International, University of Guelph, Alberta Machine Intelligence Institute (AMII), University of Quebec at Rimouski, Toyota Motor Manufacturing Canada, Dupont Sustainable Solutions

SCALE AI's investment: $1 million

Total investment: $2.9 million

Industrial video analytics uses computer vision, AI and machine learning technologies to study motion patterns of production processes and identify opportunities for improvement or help explain non-standard unexpected losses of time or motion. Current continuous improvement techniques are very manual and time consuming, and not very effective. Using AI to process large amounts of data, Pacefactory, along with its consortium partners, seeks to develop advanced video analytics solutions and processes to solve some of the costliest production problems in manufacturing.

"In this project, Magna International will participate in the development of AI video analytics solutions to optimize supply chain in their Canadian operations. Their manufacturing facilities will provide real case studies thus offering added value in the development of Pacefactory technology and improving logistics' effectiveness.", mentions Sean Clare, Co-Founder of Pacefactory.

Developing an AI-Driven Platform for Last Mile Delivery and Home Installation

Partners: Quick Contractors,

Canadian Tire, Genaire Transport, Tech Consultants Group (TCG)

SCALE AI's investment: $0.65 million

Total investment: $2 million

QuickContractors.com Inc. has partnered with Canadian Tire Corporation Ltd. to develop a SaaS AI platform that combines real-time data and AI to optimize last-mile delivery and product installations for large Canadian retailers.

The company is developing an intelligent platform with robust optimization capabilities that will synthesize existing and real-time data inputs, including inventory availability, delivery carrier details, delivery locations, GSP, contractor skill levels and more, to improve the capacity planning, scheduling and logistics of over 5000+ national contractors and delivery agents.

"This project will enable Quick Contractors to provide a cutting-edge customer experience, minimize excess labor capacity and harmonize the scheduling of heterogeneous resources to its retail partners, enabling significant AI-powered efficiency gains in last-mile supply chain management.", mentions Trevor Bouchard, CEO.

E2E ML-based Demand Forecasting

Partners: Unilever Canada, Larus Technologies, SOSCIP

SCALE AI's investment: $0.5 million

Total investment: $2.2 million

Because it offers everyday consumer products in retailers across the country, Unilever's supply chain is central to its success. By utilizing internal and external data, the End To End- and Machine-Learning-based Demand Forecasting solution, developed by Unilever Canada's Collective Intelligence and AI/ML analytics strategic partner (Larus Technologies) and supported by SOSCIP consortium smart computing platform, ensures optimal forecasting for Unilever.

Gary Wade, President, Unilever Canada: "Accurately forecasting product demand is essential for Unilever as a fast-moving consumer goods company, permitting us to optimize production and distribution, while making essential products available to all Canadians. Using unique AI expertise to provide data driven insights, this project will have a significant impact across the broader supply chain, thus benefiting all stakeholders."

Precision Harvest

Partners: McCain, Fiddlehead, Resson, Riverview Farms Corp, CP Farms Ltd, Swansfleet Alliance, Valley Farms

SCALE AI's investment: $0.75 million

Total investment: $1.8 million

Through this proposal, partners want to harness the power of data and predictions to give farmers better tools to be proactive about managing their crop and diminishing the impacts of weather uncertainty. The project will help growers in sequencing their fields for harvest against objective criteria (e.g., canopy health, wet spots, etc.) to maximize yield.

"The data and predictions will also support production planning decisions at McCain, helping us optimize potato pile management, reduce waste and control costs. This in turn supports the Canadian food supply chain overall, by reducing the need to source potatoes from other countries and reducing costs throughout the food value chain that, ultimately, impacts restaurant operators and consumers.", explains Caroline Morissette, VP Data & Analytics.

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SCALE AI announces its largest financing round of 2021 with twelve projects and $71 million in investments in artificial intelligence - Yahoo Finance

uPath Ki-67 (30-9) image analysis, uPath ER (SP1) image analysis and uPath PR (1E2) image analysis for breast cancer use pathologist-trained deep learning algorithms to enable quick calculation of Ki-67, ER and PR tumour cell nuclei positivity. This includes a whole slide analysis workflow with automated pre-computing of the slide image prior to pathologist assessment, and a clear visual overlay highlighting tumour cells with and without nuclear staining. uPath Ki-67 (30-9) image analysis, uPath ER (SP1) image analysis and uPath PR (1E2) image analysis for breast cancer produce actionable assessments of scanned slide images that are objective and reproducible, aiding pathologists in quantification of these breast cancer markers.

Intended for use with Roche's high medical value assays and slides stained on a BenchMark ULTRA instrument using ultraView DAB detection kit, the uPath Ki-67 (30-9) image analysis, uPath ER (SP1) image analysis and uPath PR (1E2) image analysis algorithms are ready-to-use and integrated within Roche's uPath enterprise software and NAVIFYDigital Pathology, the cloud version of uPath. These algorithms are for Research Use Only. Not for use in diagnostic procedures.

"Roche is committed to the expansion of digital pathology solutions to address unmet medical needs and breast cancer diagnostics is a key opportunity area. Innovations like image analysis algorithms have the potential to impact patient care by increasing the information available to pathologists and enhancing diagnostic confidence," said Jill German, Head of Roche Diagnostics Pathology Customer Area.

In December 2021, Roche will be presenting an abstract (# P1-02-17) on our artificial intelligence, deep learning development of our breast panel RUO algorithms at the San Antonio Breast Cancer Symposium, which features the latest research and development on breast cancer research. To find out more about this symposium, visit https://www.sabcs.org/Symposium-Overview-2021.

About Roche Digital Pathology

As the leading provider of pathology lab solutions, Roche is delivering an end-to-end digital pathology solution from tissue staining to producing high-quality digital images that can be reliably assessed using automated clinical image analysis algorithms.

Whole slide imaging combined with modern artificial intelligence (AI)-based image analysis tools have the potential to transform the practice of pathology. The use of AI and deep learning methods to interpret whole slide images in digital pathology enables pathologists to derive novel and meaningful diagnostic insights from tissue samples. AI-based image analysis automates quantitative tasks and enables fast, repeatable evaluation of information-rich tissue images that are sometimes difficult to interpret manually. AI-based image analysis uncovers aspects that are invisible to the human eye and reduces the risk of human error. Patients, whose tissue samples are analysed using AI-based image analysis, can benefit from a faster and more accurate diagnosis with IVD products. The insights gained from these analyses can help pathologists determine the best treatment option for cancer patients.

Roche offers two deployment options for its uPath software: an on-premise solution and a cloud solution, marketed as NAVIFY Digital Pathology. The VENTANA DP 200 slide scanner and Roche uPath enterprise software are CE-IVD marked for in-vitro diagnostic use and are available in the U.S. for Research Use Only (RUO). Not for use in diagnostic procedures. Image analysis algorithms developed by third-party entities and their utilisation are the responsibility of the third party provider.

About breast cancerBreast cancer is the second most common cancer in the world, with an estimated 2.3 million new cancer cases diagnosed in 20201 (12% of all cancers) and is the most common cancer in women globally,. (see references at bottom)

About RocheRoche is a global pioneer in pharmaceuticals and diagnostics focused on advancing science to improve people's lives. The combined strengths of pharmaceuticals and diagnostics, as well as growing capabilities in the area of data-driven medical insights help Roche deliver truly personalised healthcare. Roche is working with partners across the healthcare sector to provide the best care for each person.

Roche is the world's largest biotech company, with truly differentiated medicines in oncology, immunology, infectious diseases, ophthalmology and diseases of the central nervous system. Roche is also the world leader in in vitro diagnostics and tissue-based cancer diagnostics, and a frontrunner in diabetes management. In recent years, Roche has invested in genomic profiling and real-world data partnerships and has become an industry-leading partner for medical insights.

Founded in 1896, Roche continues to search for better ways to prevent, diagnose and treat diseases and make a sustainable contribution to society. The company also aims to improve patient access to medical innovations by working with all relevant stakeholders. More than thirty medicines developed by Roche are included in the World Health Organization Model Lists of Essential Medicines, among them life-saving antibiotics, antimalarials and cancer medicines. Moreover, for the thirteenth consecutive year, Roche has been recognised as one of the most sustainable companies in the Pharmaceuticals Industry by the Dow Jones Sustainability Indices (DJSI).

The Roche Group, headquartered in Basel, Switzerland, is active in over 100 countries and in 2020 employed more than 100,000 people worldwide. In 2020, Roche invested CHF 12.2 billion in R&D and posted sales of CHF 58.3 billion. Genentech, in the United States, is a wholly owned member of the Roche Group. Roche is the majority shareholder in Chugai Pharmaceutical, Japan. For more information, please visit http://www.roche.com

VENTANA, BENCHMARK, ultraView and UPATH are trademarks of Roche. Other product names and trademarks are the property of their respective owners.

Derrick Fennell Roche Tissue Diagnostics Communications 520-203-1757 [emailprotected]

SOURCE Roche

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Roche announces the release of its newest artificial intelligence based digital pathology algorithms to aid pathologists in evaluation of breast...

Officials at the Department of Veterans Affairs are looking to increase efficiency and optimize their clinicians professional capabilities, featuring advanced artificial intelligence and machine learning technologies.

In a November presolicitation, the VA seeks to gauge market readiness for advanced healthcare device manufacturing, ranging from prosthetic solutions, surgical instruments, and personalized digital health assistant technology, as well as artificial intelligence and machine learning capabilities.

Dubbed Accelerating VA Innovation and Learning, or AVAIL, the program is looking to supplement and support agency health care operations, according to Amanda Purnell, an Innovation Specialist with the VA

What we are trying to do is utilize AI and machine learning to remove administrative burden of tasks, she told Nextgov.

The technology requested by the department will be tailored to areas where a computer can do a better, more efficient job than a human, and thereby give people back time to complete demanding tasks that require human judgement.

Some of these areas the AI and machine learning technology could be implemented include surgical preplanning, manufacturing submissions, and 3D printing, along with injection molding to produce plastic medical devices and other equipment.

Purnell also said that the VA is looking for technology that can handle the bulk of document analyses. Using machine learning and natural language processing to scan and detect patterns in medical images, such as CT scans, MRIs and dermatology scans is one of the ways the VA aims to digitize its administrative workload.

Staff at the VA is currently tasked with looking through faxes and other clinical data to siphon it to the right place. AVAIL would combine natural language processing to manage these operations and add human review when necessary.

Purnell said that the forthcoming technology would emphasize streamlining processes that are better and faster done by machines and allowing humans to do something that is more kind of human-meaningful, and also allowing clinicians to operate to the top of their license.

She noted that machines are highly adept at scanning and analyzing images with AI. The VA procedure would likely have the AI technology to do a preliminary scan, followed by a human clinician to make their expert opinion based on results.

With machine learning handling the bulk of these processes along with other manufacturing and designing needs, clinicians and surgeons within the VA could focus more on applying their medical and surgical skills. Purnell used the example of a prosthetist getting more time to foster a human connection with a client rather than oversee other health care devices and manufacturing details.

It is making sure humans are used to their best advantage, and that were using technology to augment the human experience, she said.

The AVAIL program also stands to improve the ongoing modernization effort of the VAs beleaguered electronic health record (EHR) system, which has suffered deployment hiccups thanks to difficult interfaces and budget constraints.

The AI and machine learning technology outlined in the presolicitation could also support new EHR infrastructure and focus on an improved user experience, mainly with an improved platform interface and other accessibility features.

Purnell underscored that having AI manage form processing and data sharing capabilities, including veteran claims and benefits, is another beneficial use case.

Were alleviating that admin burden and increasing the experience both for veterans and our clinicians, in that veterans are getting more facetime with our clinicians and clinicians are doing more of what they are trained to do, Purnell said.

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VA Aims To Reduce Administrative Tasks With AI, Machine Learning - Nextgov

In light of a recent Cybereason research report, Organizations at Risk: Ransomware Attackers Dont Take Holidays, regarding the prevalence of ransomware attacks that occur during off-hours, its imperative that we look towards robust AI security solutions, such that you can know your organization is protected even when none of your staff is online.

Effective defense against the holistic issue of ransomware is much larger than just a technology problem. At its highest level, the fight against ransomware is a race against time. From the moment an attacker enters your environment, they stay there as long as necessary engaging in subtle yet detectable activity long before encrypting any of your data.

The reason for this is that ransomware is financially motivated, meaning that attackers want to exfiltrate enough sensitive information and infiltrate as many systems as they can in order to demand the highest ransom possible, making their attack operations very methodical and intentional.

Predictive Ransomware Protection, a revolutionary AI-based endpoint protection solution, detects attacks at the earliest stages in real-time by bringing artificial intelligence to each and every endpoint.

In order for your organization to effectively defend against complex ransomware attacksor RansomOpssophisticated and artificially intelligent endpoints are required. An effective solution employs a combination of AI-Powered NGAV Engines and File Manipulation Detection.

Its no secret that legacy AV just doesnt hold up anymore. Ransomware attackers themselves use artificial intelligence and highly sophisticated tools to automate aspects of their attacks, and new ransomware strains or re-packed binaries are continually developed that render traditional antivirus solutions useless.

Next-generation antivirus goes beyond simply monitoring for already known Indicators of Compromise (IOCs) by detecting attacks based on Indicators of Behavior (IOBs), the more subtle chains of potentially malicious behavior.

This is critical because the complex RansomOps of today, as mentioned above, are multifaceted operations which occur in distinct phases that only an AI-powered behavioral analytics solutions like NGAV can prevent and detect early in the attack chain.

NGAV includes several layers of protection to address each of the following types of threats:

All ransomware strains are forms of malware (however, not all malware are ransomware); highly effective malware preventionsuch as that provided by AI-Powered NGAV Engineshas proven to be a formidable preventative control against commoditized and never before seen ransomware strains.

In some novel cases, attackers will be able to bypass the multi-layered defenses of advanced NGAV. When an attacker is able to get far enough in their campaign to begin performing encryption, it is important that a solution provides a fail-safe to prevent wide-spread encryption and thus eliminating the attackers ability to demand a ransom.

File Manipulation Detection provides a means to predict and respond to an attack before it propagates far enough to disrupt business operations. This technique analyzes files at the kernel layerbelow the operating systemsuch that it is able to detect initiation of the encryption process of a file at the most fundamental level.

With this deep visibility extending as far as the binary level for each file, machine learning algorithms provide a combination of novel and sophisticated techniques such as Natural Language Detection, Binary Similarity Analysis, and extension-alteration identification and other advanced approaches to battling encryption.

By evaluating the structural make-up of the document contents, Natural Language Detection identifies when the written sentences in a file are becoming jumbled, indicating the earliest signs of encryption.

Binary Similarity Analysis leverages a technique known as fuzzy matching, meaning that it calculates a significant level of difference between file contents that enables malicious alterations to be identified. Monitoring files at the binary level enables this analysis to detect when the contents of the file are being randomized, indicating malicious activity.

Modern cybersecurity solutions, such as Predictive Ransomware Protection, provide a significant advantage over other endpoint protection approaches by employing Global File Manipulation Detection to protect local and network files. Solutions that cannot deliver artificial intelligence at the endpoint leave an organization vulnerable to mass encryption. With kernel-level visibility, a full scale ransomware attack can be predicted and prevented, ensuring that business operations can continue without interruption.

The Cybereason Predictive Ransomware Protection solution is unmatched in the industry, and is why Cybereason remains undefeated in the fight against ransomware. Learn more about ransomware defense here or schedule a demo today to learn how your organization can stay undefeated.

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Under the Hood: What Artificial Intelligence on the Endpoint Looks Like - Security Boulevard

Highlights in the use of artificial intelligence in 2021

The year 2021 was profoundly challenging for citizens, companies, and governments around the world. As covid-19 spread, requiring far-reaching health and safety restrictions, artificial intelligence (AI) applications played a crucial role in saving lives and fostering economic resilience. Research and development (R&D) to enhance core AI capabilities, from autonomous driving and natural language processing to quantum computing, continued unabated.

It typically takes years, if not decades, to develop a new vaccine. But by March 2020, vaccine candidates to fight covid-19 were already undergoing human tests, just three months after the first reported cases. The record speed of vaccine development was partly thanks to AI models that helped researchers analyze vast amounts of data about coronavirus.

There are tens of thousands of subcomponents to the outer proteins of a virus. Machine learning models can sort through this blizzard of data and predict which subcomponents are the most immunogenici.e., capable of producing an immune responseand thereby guide researchers in designing targeted vaccines. The use of AI in vaccine development may revolutionize the way all vaccines are created in the future.

Autonomous driving technology continued to mature in 2021, with the industrys leading companies testing driverless cars and opening up robotaxi services to the public in various cities. Fully automated driving, which enables rides without a human safety driver on board, will be necessary for the scalability and commercialization of autonomous driving.

In 2021, natural language systems became significantly more advanced at processing aspects of human language like sentiment and intent, generating language that aligns with human speaking and writing patterns, and even visual understanding, meaning the capability to express understanding about an image through language. These natural language models are powering more accurate search results and more sophisticated chatbots and virtual assistants, leading to better user experiences and creating value for businesses. Visual understanding lays the foundation for computer systems to physically interact in everyday scenes, as it involves both understanding visual content and expressing it through language. It will be crucial for improving the quality of human-machine interaction.

Quantum computing made significant inroads in 2021, including the Jiuzhang computers achievement of quantum supremacy. This carries significance for AI, since quantum computing has the potential to supercharge AI applications compared to binary-based classical computers. For example, quantum computing could be used to run a generative machine learning model through a larger dataset than a classical computer can process, thus making the model more accurate and useful in real-world settings. Advanced technologies such as deep learning algorithms are also playing an increasingly critical role in the development of quantum computing research.

AI hardware continued to develop in 2020, with the launch of several AI chips customized for specialized tasks. While an ordinary processor is capable of supporting AI tasks, AI-specific processors are modified with particular systems that can optimize performance for tasks like deep learning. As AI applications become more widespread, any increase in performance or reduction in cost can unlock more value for companies that operate a wide network of data centers for commercial cloud services, and can facilitate the companys internal operations.

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Top Remarkable Artificial Intelligence Developments that Happened in 2021 - Analytics Insight