Category Archives: Artificial Intelligence

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To say that AI is complicated is an understatement. Machine learning, a subset of artificial intelligence, is a multifaceted process that integrates and scales mountains of data that comes in different forms from various sources. Data is used to train machine learning models in order to develop insights and solutions from newly acquired related data. For example, an image recognition model trained with several million dog and cat photos can efficiently classify a new image as either a cat or a dog.

A better way to build and manage machine learning models

Project Codeflare

The development of machine learning models requires the coordination of many processes linked together with pipelines. Pipelines can handle data ingestion, scrubbing, and manipulation from varied sources for training and inference.Machine learning models use end-to-end pipelines to manage input and output data collection and processing.

To deal with the extraordinary growth of AI and its ever-increasing complexity, IBM created an open-source framework calledCodeFlareto deal with AIs complex pipeline requirements. CodeFlaresimplifies the integration, scaling, and acceleration of complex multi-step analytics and machine learning pipelines on the cloud.Hybrid cloud deployment is one of the critical design points for CodeFlare, which using OpenShift can be easily deployed from on-premises to public clouds to edge.

It is important to note thatCodeFlare is not currently a generally available product, and IBM has yet to commit to a timeline for it becoming a product. Nevertheless, CodeFlare is available as an open-source project.And, as an evolving project, some aspects of orchestration and automation are still work in progress. At this stage, issues can be reported through the public GitHub project. IBM invites community engagement through issue and bug reports, which will be handled on a best effort basis.

CodeFlares main features are:

Technology

CodeFlare is built on top of Ray, an open-source distributed computing framework for machine learning applications. According to IBM, CodeFlare extends the capabilities of Ray by adding specific elements to make scaling workflows easier. CodeFlare pipelines run on a serverless platform using IBM Cloud Code Engine and Red Hat OpenShift. This platform providesCodeFlare the flexibility to be deployed just about anywhere.

Emerging workflows

Emerging AI/ML workflows pose new challenges

CodeFlare can integrate emerging workflows with complex pipelines that require integration and coordination of different tools and runtimes. It is designed also to scale complex pipelines such as multi-step NLP, complex time series and forecasting, reinforcement learning, and AI-Workbenches. The framework can integrate, run, and scale heterogenous pipelines that use data from multiple sources and require different treatments.

How much difference does CodeFlare make?

According to theIBM Research blog, CodeFlare significantly increases the efficiency of machine learning. The blog states that a user used the framework to analyze and optimize approximately 100,000 pipelines for training machine learning models. CodeFlare cut the time it took to execute each pipeline from 4 hours to 15 minutes - an 18x speedup provided by CodeFlare.

The research blog also indicates that CodeFlare can save scientists months of work on large pipelines, providing the data team more time for productive and development work.

Wrapping up

Studies show that about75%of prototype machine learning models fail to transition to production status despite large investments in artificial intelligence. Several reasons for low conversion rates range from poor project planning to weak collaboration and communications between AI data team members.

CodeFlare is a purpose-built platform that provides complete end-to-end pipeline visibility and analytics for a broad range of machine learning models and workflows. It providesa more straightforward way to integrate and scale full pipelines while offering a unified runtime and programming interface.

For those reasons, despite the historical high AI model failure rates, Moor Insights & Strategy believes that machine learning models using CodeFlare pipelines will have a high percentage of machine learning models transition from experimental status to production status.

Analyst Notes:

Note: Moor Insights & Strategy writers and editors may have contributed to this article.

Moor Insights & Strategy, like all research and analyst firms, provides or has provided paid research, analysis, advising, or consulting to many high-tech companies in the industry, including 8x8, Advanced Micro Devices, Amazon, Applied Micro, ARM, Aruba Networks, AT&T, AWS, A-10 Strategies,Bitfusion, Blaize, Box, Broadcom, Calix, Cisco Systems, Clear Software, Cloudera,Clumio, Cognitive Systems, CompuCom, Dell, Dell EMC, Dell Technologies, Diablo Technologies, Digital Optics,Dreamchain, Echelon, Ericsson, Extreme Networks, Flex, Foxconn, Frame (now VMware), Fujitsu, Gen Z Consortium, Glue Networks, GlobalFoundries, Google (Nest-Revolve), Google Cloud, HP Inc., Hewlett Packard Enterprise, Honeywell, Huawei Technologies, IBM, Ion VR,Inseego, Infosys, Intel, Interdigital, Jabil Circuit, Konica Minolta, Lattice Semiconductor, Lenovo, Linux Foundation,MapBox, Marvell,Mavenir, Marseille Inc, Mayfair Equity, Meraki (Cisco),Mesophere, Microsoft, Mojo Networks, National Instruments, NetApp, Nightwatch, NOKIA (Alcatel-Lucent), Nortek,Novumind, NVIDIA, Nuvia, ON Semiconductor, ONUG, OpenStack Foundation, Oracle, Poly, Panasas,Peraso, Pexip, Pixelworks, Plume Design, Poly,Portworx, Pure Storage, Qualcomm, Rackspace, Rambus,RayvoltE-Bikes, Red Hat,Residio, Samsung Electronics, SAP, SAS, Scale Computing, Schneider Electric, Silver Peak, SONY,Springpath, Spirent, Splunk, Sprint, Stratus Technologies, Symantec, Synaptics, Syniverse, Synopsys, Tanium, TE Connectivity,TensTorrent,TobiiTechnology, T-Mobile, Twitter, Unity Technologies, UiPath, Verizon Communications,Vidyo, VMware, Wave Computing,Wellsmith, Xilinx, Zebra,Zededa, and Zoho which may be cited in blogs and research.

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Create And Scale Complex Artificial Intelligence And Machine Learning Pipelines Anywhere With IBM CodeFlare - Forbes

The Tanner Humanities Center at the University of Utah will host Shoshana Zuboff, author of The Age of Surveillance Capitalism: The Fight for a Human Future at the New Frontier of Power, for the Obert C. Tanner Lecture on Artificial Intelligence and Human Values, Thursday, Oct. 28 at noon. Her lecture will explore the Digital Revolution entwined with the evolution of capitalism. The online event is free and open to the public, but registration is required.

Shoshana Zuboffs scholarship is an important call to action that makes clear the consequences of an uncritical approach to information technology development for human relationships, human rights and the human experience, said Erika George, director of the Tanner Humanities Center. Her career is a testament to the power of sustained research and careful observation to illuminate the big picture issues shaping our society.

Zuboff is the author of three books, each of which signaled the start of a new epoch in technological society. In the late 1980s she foresaw how computers would revolutionize the modern workplace. Writing before the invention of the iPod or Uber, she predicted the rise of digitally mediated products and services tailored to the individual. She also warned of the individual and societal risks.

Now her masterwork, The Age of Surveillance Capitalism: The Fight for a Human Future at the New Frontier of Power, synthesizes years of research to reveal a world in which technology users are neither customers, employees, nor products. Instead, she argues they are the raw material for new procedures of manufacturing and sales that define an entirely new economic order a surveillance economy. She invites alternative approaches.

This event is sponsored by O.C Tanner and the University of Utah College of Humanities.

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Tanner Lecture on Artificial Intelligence and Human Values | @theU - @theU

New European Union rules governing artificial intelligence (AI) will put compliance obligations on automated facial recognition (AFR) used in some regtech applications, particularly client risk screening. UK data privacy and biometrics regulators are also seeking to improve employee monitoring and surveillance camera operation guidance to clarify compliance obligations under local data privacy laws. These efforts, in conjunction with existing data privacy laws, could prompt firms to reduce or even eliminate facial recognition technology's compliance and workplace applications.

Financial institutions use facial recognition in compliance applications including for anti-money laundering (AML) and know-your-customer (KYC) risk screening, communications recordkeeping and video conferencing surveillance, as well as to monitor employees.

These legislative and regulatory efforts will not result in a total ban on facial recognition, but some regulators would like to see private and commercial AFR use banned or conducted under licence only. The technology is liable to serious misuse and has the potential for societal detriment, said Fraser Sampson, the UK's surveillance camera commissioner.

"There are potentially some developments in this area which are so ethically fraught, that instead of the system waiting for something that's been done, and then tries to show that it was wrong. You do it the other way and say 'no the only the only circumstances under which you will be able to do this lawfully is if you are licensed to do so'," Sampson told Regulatory Intelligence.

The AI Act

The European Union's draft Artificial Intelligence Act ( AI Act) will ban most AFR use by law enforcement and will deem regtech applications such as KYC/AML risk screening to be biometric identification and categorisation of natural persons products, and therefore high risk. That means these AI-powered compliance tools like KYC/AML risk screening will themselves be subject to a range of compliance obligations data and data governance, transparency for users, human oversight, accuracy, robustness and cyber security, as well as traceability and auditability.

Financial institutions use facial recognition for identity verification in client onboarding KYC processes. This use would generally be deemed "limited risk" if an onboarding system checks whether a customer's submitted picture matches the one used on the identification document (such as a passport photo) they supplied. There would still be some transparency requirements and disclosure obligations under the AI Act.

"With regards to the draft AI Act, there are different levels of risks. There are systems that are restricted, for example, real-time biometric identification, which is prohibited. Then you have the high-risk systems, which would be biometric identification. But if you're a bank, and you're conducting KYC processes with regards to verification, then that will not fall under high risk, which is why the banks will stay away from identification. AFR in the banks ... is not going to be coming any time soon," said Jimmy Orucevic, a privacy professional in KPMG's cyber information management and compliance practice in Zurich.

Some AML/KYC vendors are marketing facial recognition systems which check customers against a private database of "unnamed people of interest" as an alternative to name-based screening. Vendors say this technology allows firms to identify emerging threats posed by unnamed persons of interest not covered by traditional watchlists. This application looks like identification, not verification, which would land it in the high-risk category.

"I don't see how banks, especially in Switzerland, would ever touch a tool like that, but I haven't seen it yet. Even under the [General Data Protection Regulation] or under the Swiss Data Protection Federal Act, there will be a lot of obligations coming with implementing such a tool," Orucevic said.

Members of the European Parliament this week asked for a permanent ban on the automated recognition of individuals in public spaces, noting that citizens should only be monitored when suspected of a crime. MEPs said the "use of private facial recognition databases (such as the Clearview AI system) and predictive policing based on behavioural data" should be forbidden.

Commercial use of such private facial recognition databases could come under further scrutiny. MEPs will consider high-risk categories, types of technology, and their applications as they prepare their position on the AI Act, a European Parliament spokesperson for civil liberties, justice and home affairs told Regulatory Intelligence.

AFR for MiFID recordkeeping

Some firms now deploy Markets in Financial Instruments Directive (MiFID II) recordkeeping tools that capture and store video with the ability to use AI and AFR to search for misconduct. Vendors are also marketing security systems that use AFR to monitor employees' on-camera conduct and content-sharing, and intervene in real time. This kind of application will likely be designated high-risk once the AI Act is passed.

"The biggest problem is when you have the MiFID regulations, you need to record every call. That's why a lot of major banks don't allow video calls. If you do allow videos, then you need some kind of artificial intelligence to sort out the irrelevant videos from the relevant ones. The question is, what are you using there to look for voice or a picture? If this is using artificial intelligence or facial recognition, you must be doing it right otherwise you will run into problems with the law," said Alberto Job, director, information management and compliance, at KPMG in Zurich.

Behavioural monitoring and surveillance aimed at measuring productivity such as keystroke tracking is already forbidden in Europe, Job said.

The AI Act is at the beginning of the legislative process, meaning it could be five years before regulations apply.

Control, not compliance

The use of employee surveillance technology, including AFR, grew during the pandemic, with many financial institutions insisting employees working from home were "on camera" throughout their working day. Firms cited compliance and productivity as reasons for heightened surveillance.

Firms are using surveillance technology to control employees working from home, rather than for compliance purposes, said J.S. Nelson, a visiting associate professor at Harvard Business School who specialises in management practices, compliance and surveillance.

"What's going on is, it's not just compliance. Compliance I think of as enforcing legal standards. It's a baseline. What you have here is compliance as a cover for these arguments about the productivity and efficiency of workers. It's a management argument. That's what's really going on. That's that issue of control. That issue of 'I feel out of control', because the very privileged few who are able to work from home, they've disappeared from the office," she said.

Surveillance programmes appeal to managers' sense of control and being on top of what is happening with employees and their productivity, Nelson said.

UK ICO to enhance employee surveillance guidance

The UK Information Commissioner's Office (ICO) is reviewing its surveillance guidance following more widespread adoption during the pandemic not only to monitor for employee illness, but also, primarily, to watch employees working from home.

"There's no way to separate your work life from your personal life, especially when the workplace is your home and you're being surveilled constantly. It's really insidious. You have all this pushing of boundaries which is the place in which you're surveilled, the times at which you're surveilled, and the purposes for which you're surveilled. We have this capacity to get all this data and save it all up, but haven't had the conversation that we really need to about why this stuff is collected, for what purpose and what the limit should be of it? Then there's the question of how it's experienced, because it's changing the nature of work and changing our experience of the workplace itself," Nelson said.

The ICO already has some guidance on surveillance camera use, saying employees may not always expect to be monitored via video surveillance systems in their day-to-day roles. Employers should consider any less privacy-intrusive ways to achieve the same result and make an assessment of its necessity and proportionality.

Many financial services firms view surveillance of all kinds as part of the terms of employment. "Work here and submit to surveillance for compliance purposes" is the common explanation. That changes in the home environment when AFR is used.

"There are two broad approaches you can take whereby you say everything is in scope except for those areas that you argue ought not to be. Or you can begin by saying, 'no this is enormously intrusive and therefore the starting point is you can't do any of this unless each aspect has a compelling and cogent reason behind it'. The approach you take is determined by the imbalance of power. If I'm an employee, how much influence do I have on which things I will consent to readily or at all?" Sampson said.

UK surveillance camera code

The UK government is consulting on new guidance for surveillance camera operation. It will apply to surveillance cameras used by public bodies and law enforcement, including AFR. Nevertheless, private and commercial AFR users should take note to avoid data privacy breaches, and follow the data collection minimisation principles.

Cameras built into a work laptop and connected to an employer-monitored system are surveillance cameras, Sampson said. Sometimes these cameras link to facial recognition software which require employees to log in continuously, to confirm they are at their desks. Firms operating surveillance camera systems inside employees' homes risk capturing images of employees' children, for example, which would constitute a data privacy breach.

Last year the Court of Appeal, in Bridges v South Wales Police, found South Wales Police's use of facial recognition technology breached privacy rights, data protection laws and equality laws.

There is a read-across for private or commercial AFR users conducting surveillance including employee monitoring, Sampson said.

"Looking at Bridges as a lawyer, my view remains that it is by and large a data protection principles case that emerged in the context of a surveillance camera. Very little that took place in terms of the argued points in Bridges were solely related to surveillance camera use by the police. Those points were and are applicable to most other settings and are of general application. In the same way that some of the other employee monitoring cases that have come from Strasbourg apply the principles, and then say, within the setting of an employment arrangement, this is is how they should be interpreted. But there's still general central tenets of people's rights when they live in a mature and tolerant democracy," he said.

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EU rules governing artificial intelligence will put compliance obligations on facial recognition regtech - JD Supra

The Pentagon's former software chief resigned and said that China is headed toward global dominance in artificial intelligence due to the relatively slow pace of innovationin the United States.

"We have no competing fighting chance against China in 15 to 20 years. Right now, its already a done deal; it is already over in my opinion," the Pentagon's former software chief, Nick Chaillan, told the Financial Times, adding that some of the U.S.'s cyber defense systems wereat "kindergarten level."

Chaillanannounced his resignation last month as an act of protest against the United States' slow pace of tech development.Chaillan saidAmerica's failure toaggressively pursue AI capacity was putting the nation at risk, according toReuters.

In the next decade, Western intelligence reportspredictChinawill dominate with many emerging technologies like AI, synthetic biology and genetics, Reutersreported.

Chaillan also attributed the sluggish pace to companies like Google hesitating to work with the government on AI andongoing debates about AI ethics in the U.S., while China pushes forward without consideration for the potential ethical consequences.

"Google is proud to work with the U.S. government, and we have many projects underway today, including with the Department of Defense, Department of Energy, and the NIH," a Google Cloud spokesperson said in a statement to The Hill. "We are committed to continuing to partner with the U.S. government, including the military, both on specific projects and on broader policy around AI that are consistent with our principles."

Meanwhile, Secretary of Defense Lloyd J. Austin III in July recognized that "China is our pacing challenge" when it comes to AI development.

"Were going to compete to win, but were going to do it the right way,"Austin said."Were not going to cut corners on safety, security, or ethics."

Ina LinkedIn post announcing his departure on Sept. 2, Chaillan insisted that the U.S. could not "afford to be behind."

"If the US cant match the booming, hardworking population in China, then we have to win by being smarter, more efficient, and forward-leaning through agility, rapid prototyping and innovation. We have to be ahead and lead."

Chaillan was also critical of the Department of Defense and its decisions to put people with limitedIT experience in leadership roles over software programs.

"The DoD should stop pretending they want industry folks to come and help if they are not going to let them do the work. While we wasted time in bureaucracy, our adversaries moved further ahead," Chaillan said.

"I will always feel some guilt or regret in leaving. I have this sinking feeling that I am letting our warfighters, the teams, and my children down by not continuing to fight for a better outcome 20 years from now,"Chaillan added of his departure.

Updated at 2:28 p.m.

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Former Pentagon official says China has won artificial intelligence battle | TheHill - The Hill

ModelOps is a set of automated practices and tools that help deploy, manage, monitor, and improve models in production. The approach is designed to be model-centric, which means everything is instrumented around the model, from deployment to governance to inference and monitoring to scale.

Far and wide, investment in artificial intelligence and machine learning are drastically increasing and new data science projects are underway to build predictive and analytical models for various purposes. However, while companies plan to scale up sophisticated Artificial Intelligence solutions in a reasonable time, the harsh reality is that the adoption of these solutions is often stalled because companies generally focus more on development than on the operationalization of the models. On that note, ModelOps comes to the rescue bringing advancements in AI.

Since the ModelOps approach brings all the players together, several emerging start-ups, as well as enterprise companies, offer ModelOps solutions to orchestrate these components collectively in an end-to-end fully automated model life cycle. Let us have a look at the figure below showing how by managing a platform enterprises can govern and scale any AI initiatives.

Powerful platforms like ModelOp center typically integrate with development platforms, IT systems, and enterprise applications so that businesses can leverage and extend ongoing investments in AI and IT. In this way, data scientists can work at scale using the tools they know best.

AI answers Distress and Help-calls

Emergency relief services are flooded with distress and help calls in the event of any emergency. Managing such a huge number of calls is time-consuming and expensive when done manually. The chances of critical information being lost or unobserved are also a possibility. In such cases, AI can work as a 24/7 dispatcher. AI systems and voice assistants can analyze massive amounts of calls, determine what type of incident occurred and verify the location. They can not only interact with callers naturally and process those calls, but can also instantly transcribe and translate languages. AI systems can analyze the tone of voice for urgency, filtering redundant or less urgent calls and prioritizing them based on the emergency.

Machine learning and other data science approaches are not limited to assisting the on-ground relief teams or assisting only after the actual emergency. Machine learning approaches such as predictive analytics can also analyze past events to identify and extract patterns and populations vulnerable to natural calamities. A large number of supervised and unsupervised learning approaches are used to identify at-risk areas and improve predictions of future events. For instance, clustering algorithms can classify disaster data based on severity. They can identify and segregate climatic patterns which may cause local storms with the cloud conditions which may lead to a widespread cyclone.

Predictive machine learning models can also help officials distribute supplies to where people are going, rather than where they were by analyzing real-time behavior and movement of people.

In addition, predictive analytics techniques can also provide insight for understanding the economic and human impact of natural calamities. Artificial neural networks take in information such as region, country, and natural disaster type to predict the potential monetary impact of natural disasters.

Recent advances in cloud technologies and numerous open-source tools have enabled predictive analytics with almost no initial infrastructure investment. So agencies with limited resources can also build systems based on data science and develop more sophisticated models to analyze disasters.

As with every progressing technology, AI will also build on its existing capabilities. It has the potential to eliminate outages before they are detected and give disaster response leaders an informed, clearer picture of the disaster area, ultimately saving lives.

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Evolution of ModelOps: Now a More Advanced Artificial Intelligence - Analytics Insight

This week in Washington IP news, Congress is largely quiet except for a hearing of the House Artificial Intelligence Task Force regarding ethical frameworks for developing artificial intelligence (AI) applications in various industries. Elsewhere in D.C., the Center for Data Innovation explores data driven approaches in addressing e-commerce counterfeits, The Brookings Institution hosts a conversation with Susteons Shantanu Agarwal on the challenges of carbon removal tech, and the U.S. Patent and Trademark Office kicks off the 2021 Hispanic Innovation and Entrepreneurship Program with multiple fireside chats and a panel on building networks and resources available to the community of Hispanic innovators.

U.S. Patent and Trademark Office

Trademark Basics Boot Camp, Module 2: Registration Process Overview

At 2:00 PM on Tuesday, online video webinar.

This workshop, the second in the USPTOs eight-part Trademark Basics Boot Camp series, is designed to teach small business owners and entrepreneurs about different aspects of the trademark registration process. Topics covered in this workshop include trademark basics, application workflow, timeline overview and post-registration workflow overview.

House Task Force on Artificial Intelligence

Beyond I, Robot: Ethics, Artificial Intelligence, and the Digital Age

At 12:00 PM on Wednesday, online video webinar.

Ethics in robotics and artificial intelligence systems draws much of its foundation from the three laws of robotics developed by famed science fiction writer Isaac Asimov, which are predicated on the idea that AI systems are always meant to serve humans and never to harm them. With the advent of many AI technologies now upon us, several organizations have been developing ethical frameworks for AI applications that rely upon constant evaluation by human decision-makers and great transparency about the underlying goals guiding the development of particular algorithms. The witness panel for this hearing will include Meredith Broussard, Associate Professor, Arthur L. Carter Journalism Institute, New York University; Meg King, Director, Science and Technology Innovation Program, The Wilson Center; Miriam Vogel, President and CEO, EqualAI; Jeffrey Yong, Principal Advisor, Financial Stability Institute, Bank for International Settlements; and Aaron Cooper, Vice President for Global Policy, BSA The Software Alliance.

U.S. Patent and Trademark Office

2021 Hispanic Innovation and Entrepreneurship Program

At 1:00 PM on Wednesday, online video webinar.

This event features various leaders from the Hispanic community in innovation and entrepreneurship and offers an overview of innovation resources that are available to the innovation community. This event will feature a pair of fireside chats featuring Alejandra Y. Castillo, Assistant Secretary of Commerce for Economic Development; Nestor Ramirez, Technology Center Director, USPTO; Leandro Margulis, Inventor of Durable Radio-Frequency Identification (RFID) Device; and Marivelisse Santiago-Cordero, Senior Advisor to the Deputy Commissioner for Patents, USPTO. This event will also feature a discussion about building networks and finding mentors with a panel including Jennifer Garcia, COO, Latin Business Action Network, Stanford Latino Entrepreneurship Initiative; Olga Carmargo, CEO and Founder, FARO Associates LLC and Board Chair, Hispanic Alliance for Career Enhancement; Susana G. Baumann, President and CEO and Editor-in-Chief, Latinas in Business Inc.; Tito Leal, CFO, Prosperity Lab; and moderated by Juan Valentin, Education Program Advisor, Office of Education, USPTO.

The Brookings Institution

Carbon Removal Innovations and Their Challenges: A Conversation With Susteon President Shantanu Agarwal

At 2:00 PM on Wednesday, online video webinar.

Carbon removal technologies that can sequester airborne sources of carbon have the potential to play a critical role in mitigating climate change, but several promising carbon removal innovations remain stuck in basic research phases far from the commercialization pipeline. This event, part of The Brookings Institutions Reimagining Modern-Day Markets and Regulations series, will feature a fireside chat with Shantanu Agarwal, Co-Founder and President of climate impact technology firm Susteon Inc. Moderating the discussion with Agarwal will be Sanjay Patnaik, Director, Center on Regulations and Markets, and the Bernard L. Schwartz Chair in Economic Policy Development, Fellow, Economic Studies.

Center for Data Innovation

A Data-Driven Approach to Combatting Counterfeit Goods in E-Commerce

At 1:00 PM on Thursday, online video webinar.

E-commerce has proved to be a boon to counterfeiters looking to exploit popular brands and fool American consumers into purchasing knockoff goods. This event will explore a new report issued by the National Intellectual Property Rights Center discussing the marketplace response to best practices developed by public and private entities looking to stem the tide of counterfeits sold via online platforms. This event will feature a discussion with a panel including Matthew C. Allen, Director, National Intellectual Property Rights Coordination Center; Christa Brozowski, Senior Manager of Public Policy, Amazon; Sara Decker, Director of Federal Government Affairs, Walmart; Piotr Stryszowski, Senior Economist, OECD; and moderated by Daniel Castro, Director, Center for Data Innovation.

U.S. Patent and Trademark Office

The Path to a Patent, Part II: Drafting Provisional Patent Applications

At 2:00 PM on Thursday, online video webinar.

This workshop, the second in the USPTOs eight part Path to a Patent series, is designed to teach prospective patent applicants about the key differences between provisional and nonprovisional patent applications. Topics covered include filing requirements, fees and different ways to file a provisional patent application.

Hudson Institute

Powering Innovation: Advanced Batteries and Critical Supply Chains

At 2:30 PM on Thursday, online video webinar.

Both the United States and China have been taking action on securing supply chains on certain products and components that are critical to national security, advanced batteries being one of the sectors identified by both nations as a supply chain priority. Advanced battery technologies have potential applications in electric vehicles, which many governments have been subsidizing to meet climate and emissions goals, as well as in national defense by enabling distributed operations in battlefield scenarios. The first panel for this event, discuss distributed operations and advanced batteries, will include Heather Penny, Senior Fellow, Mitchell Institute for Aerospace Studies; LTG Eric Wesley (Ret.), Former Deputy Commanding General, Army Futures Command, and Director, Futures and Concepts Center; Bryan Clark, Senior Fellow and Director, Center for Defense Concepts and Technology, Hudson Institute; and moderated by Nadia Schadlow, Senior Fellow, Hudson Institute. The second panel, discussing the U.S. governments role in promoting innovation, will include the Honorable Ellen Lord, Former Undersecretary of Defense for Acquisition and Sustainment; the Honorable Kimberly Reed, Former Chairman of the Board of Directors, President and CEO, U.S. Import-Export Bank; Mike Brown, Director, Defense Innovation Unit, U.S. Department of Defense; and moderated by Arthur Herman, Senior Fellow and Director, Quantum Alliance Initiative, Hudson Institute. The third panel, discussing China, supply chains and economic coercion, will include Anthony Vinci, Adjunct Senior Fellow, CNAS; Pavneet Singh, Non-Resident Senior Fellow, The Brookings Institution; John Lee, Senior Fellow, Hudson Institute; and moderated by Nadia Schadlow, Senior Fellow, Hudson Institute.

Information Technology & Innovation Foundation

Can GDPRs Automated Decision Opt-Out Be Improved Without Harming Users?

At 10:00 AM on Friday, online video webinar.

In the nearly two years that have elapsed since the UK government completed their Brexit tradition out of the European Union, the country has been charting its own course forward on legal matters and in recent weeks the UK government has been eyeing changes to Article 22 of the countrys General Data Protection Regulation (GDPR). Article 22 of the GDPR governs restrictions to automated processing of decisions for a data subject, and the UK governments moves have opened a discussion on the feasibility of changing protections against automated decision-making processes. This event will feature a discussion with a panel including Omar Tene, Former Vice President, International Association of Privacy Professionals; Isabelle de Pauw, Head of Data Rights, Domestic Data Protection and Data Rights Team, Department for Digital, Culture, Media and Sport; Chris Elwell-Sutton, Senior Privacy Counsel and Data Protection Officer, CIBC Capital Markets; Andrew Orlowski, Technology Commentator, Daily Telegraph; Kristian Stout, Director of Innovation Policy, International Center for Law & Economics; and moderated by Benjamin Mueller, Senior Policy Analyst, Center for Data Innovation.

U.S. Patent and Trademark Office

Attend the Trademark Public Advisory Committee Quarterly Meeting

At 10:00 AM on Friday, online video webinar.

On Friday morning, the Trademark Public Advisory Committee (TPAC) of the USPTO will convene their quarterly meeting to discuss issues related to the agencys trademark activities, including a review of policies, goals, budget, performance and user fees.

Image Source: Deposit PhotosAuthor: sborisovImage ID: 30853945

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This Week in Washington IP: Ethics in Artificial Intelligence, Challenges with Carbon Removal and the USPTO Hosts the 2021 Hispanic Innovation and...

A major new report on the state of artificial intelligence (AI) has just been released. Think of it as the AI equivalent of an Intergovernmental Panel on Climate Change report, in that it identifies where AI is at today, and the promise and perils in view.

From language generation and molecular medicine to disinformation and algorithmic bias, AI has begun to permeate every aspect of our lives.

The report argues that we are at an inflection point where researchers and governments must think and act carefully to contain the risks AI presents and make the most of its benefits.

The report comes out of the AI100 project, which aims to study and anticipate the effects of AI rippling out through our lives over the course of the next 100 years.

AI100 produces a new report every five years: the first was published in 2016, and this is the second. As two points define a line, this second report lets us see the direction AI is taking us in.

One of us (Liz Sonenberg) is a member of the standing committee overseeing the AI100 project, and the other (Toby Walsh) was on the study panel that wrote this particular report. Members of the panel came from across the world, with backgrounds in computer science, engineering, law, political science, policy, sociology and economics.

The report highlights the remarkable progress made in AI over the past five years. AI is leaving the laboratory and has entered our lives, having a real-world impact on people, institutions, and culture. Read the news on any given day and youre likely to find multiple stories about some new advance in AI or some new use of AI.

For example, in natural language processing (NLP), computers can now analyse and even generate realistic human language. To demonstrate, we asked Open AIs GPT-3 system, one of the largest neural networks ever built, to summarise the AI100 report for you. It did a pretty good job, even if the summary confronts our sense of self by being written in the first person:

In the coming decade, I expect that AI will play an increasingly prominent role in the lives of people everywhere. AI-infused services will become more common, and AI will become increasingly embedded in the daily lives of people across the world.

I believe that this will bring with it great economic and societal benefits, but that it will also require us to address the many challenges to ensure that the benefits are broadly shared and that people are not marginalised by these new technologies.

A key insight of AI research is that it is easier to build things than to understand why they work. However, defining what success looks like for an AI application is not straightforward.

For example, the AI systems that are used in healthcare to analyse symptoms, recommend diagnoses, or choose treatments are often far better than anything that could be built by a human, but their success is hard to quantify.

Read more: GPT-3: new AI can write like a human but don't mistake that for thinking neuroscientist

As a second example of the recent and remarkable progress in AI, consider the latest breakthrough from Googles DeepMind. AlphaFold is an AI program that provides a huge step forward in our ability to predict how proteins fold.

This will likely lead to major advances in life sciences and medicine, accelerating efforts to understand the building blocks of life and enabling quicker and more sophisticated drug discovery. Most of the planet now knows to their cost how the unique shape of the spike proteins in the SARS-CoV-2 virus are key to its ability to invade our cells, and also to the vaccines developed to combat its deadly progress.

The AI100 report argues that worries about super-intelligent machines and wide-scale job loss from automation are still premature, requiring AI that is far more capable than available today. The main concern the report raises is not malevolent machines of superior intelligence to humans, but incompetent machines of inferior intelligence.

Once again, its easy to find in the news real-life stories of risks and threats to our democratic discourse and mental health posed by AI-powered tools. For instance, Facebook uses machine learning to sort its news feed and give each of its 2 billion users an unique but often inflammatory view of the world.

Its clear were at an inflection point: we need to think seriously and urgently about the downsides and risks the increasing application of AI is revealing. The ever-improving capabilities of AI are a double-edged sword. Harms may be intentional, like deepfake videos, or unintended, like algorithms that reinforce racial and other biases.

AI research has traditionally been undertaken by computer and cognitive scientists. But the challenges being raised by AI today are not just technical. All areas of human inquiry, and especially the social sciences, need to be included in a broad conversation about the future of the field. Minimising negative impacts on society and enhancing the positives requires consideration from across academia and with societal input.

Governments also have a crucial role to play in shaping the development and application of AI. Indeed, governments around the world have begun to consider and address the opportunities and challenges posed by AI. But they remain behind the curve.

A greater investment of time and resources is needed to meet the challenges posed by the rapidly evolving technologies of AI and associated fields. In addition to regulation, governments also need to educate. In an AI-enabled world, our citizens, from the youngest to the oldest, need to be literate in these new digital technologies.

At the end of the day, the success of AI research will be measured by how it has empowered all people, helping tackle the many wicked problems facing the planet, from the climate emergency to increasing inequality within and between countries.

AI will have failed if it harms or devalues the very people we are trying to help.

Continued here:

Artificial intelligence is now part of our everyday lives and its growing power is a double-edged sword - The Conversation AU

DUBLIN, October 07, 2021--(BUSINESS WIRE)--The "Global Artificial Intelligence (AI) Enabled Drug Discovery and Clinical Trials Market Research Report: Forecast (2021-2026)" report has been added to ResearchAndMarkets.com's offering.

The "Global Artificial Intelligence (AI) Enabled Drug Discovery and Clinical Trials Market" is likely to grow at a CAGR of around 23.6% during the forecast period, i.e., 2021-26, says the author. The market growth primarily attributes to the rising demand for reducing the cost of novel drug discovery and their production. Additionally, the adoption of artificial intelligence is significantly increasing, as faster, efficient, and cost-effective drug discovery is gaining momentum amongst the pharmaceutical industry stakeholders.

The research report, states that the burgeoning volume of data generated by the molecule screening processes & preclinical studies is another crucial factor fueling the adoption of artificial intelligence, thereby propelling market growth.

Global Artificial Intelligence (AI) Enabled Drug Discovery and Clinical Trials Market Have positively impacted by COVID-19 Pandemic

With the global Covid-19 pandemic outbreak in early 2020, the pharma and biotech companies have increasingly turned to artificial intelligence to enhance precision and speed in drug development. The pandemic has set the stage for massive investments toward fast development and trials of drugs.

Moreover, the adoption of artificial intelligence helps in offering a high level of precision to the complicated and time-consuming discovery phase in the drug development process, thereby leading to faster development of drugs and lower failure risk.

Companies Mentioned

Accutar Biotechnology

Ardigen

Atomwise Inc.

AiCure LLC

Berg LLc

Cloud Pharmaceuticals

Biovista

Cyclica Inc.

Symphony Innovations

GNS Healthcare

Market Segmentation

Oncology Dominated the Global Artificial Intelligence (AI) Enabled Drug Discovery and Clinical Trials Market with largest share

Story continues

Based on the Therapeutic Application, the market bifurcates into Oncology, Cardiovascular Disease, Nervous System Disease, Respiratory Disorder, Metabolic Disorder, Immunologic Disease, and Infectious Disease. Of these segments, Oncology acquired the largest share of the Global Artificial Intelligence (AI) Enabled Drug Discovery and Clinical Trials Market in the previous few years.

AI helps in the early detection of cancer. Personalized medicine is necessary for the treatment of cancer as cancer treatments may vary for every patient. Thus, AI identifies genetic mutations, which further help oncologists design effective personalized medicine for cancer patients. Hence, these aspects aid the overall market growth cites the authors in their research report "Global Artificial Intelligence (AI) Enabled Drug Discovery and Clinical Trials Market Analysis, 2021".

Contract Research Organization (CROs) to Dominate the Global Artificial Intelligence (AI) Enabled Drug Discovery and Clinical Trials Market During the Forecast Period.

Based on the End-User, the market segments into Biopharmaceutical Industry, Contract Research Organization (CROs), and Academic Institutes & Research Centers. For improving drug discovery and clinical trials, the end-users such as biopharmaceutical companies, contract research organizations, and academic institutions are assimilating AI-enabled solutions. However, Contract Research Organization (CROs) is likely to capture a significant share of the Global Artificial Intelligence (AI) Enabled Drug Discovery and Clinical Trials Market during the forecast period.

The segment growth owes to the increasing demand for maintaining the facility workflow and rearranging the work for enhancing the quality of work. AI-enabled solutions in the CROs are gaining popularity, thereby catalyzing the overall demand for drug discovery and clinical trials.

Regional Landscape

North America Attained the Largest Market Share

The region's growth attributes to the high patient acceptance for advanced technologies and growing positive perception of AI amongst businesses. It further propels by the presence of the substantially large and well-established healthcare industry. Additionally, rising awareness regarding the benefits of integrating AI in the drug discovery process contributes to the Artificial Intelligence (AI) Enabled Drug Discovery and Clinical Trials Market growth in the region.

Market Drivers

Mounting Technological Advancement to Propel the Market Growth

Burgeoning technological advancements like AI, 4K Imaging, and IoT (Internet of Things) have resulted in surging the demand for integrating these high-end technologies in the healthcare industry. AI helps provide patient-centric treatment, better diagnostic services, and accurate decision-making, thereby recognizing errors in the current situations and providing better alternatives in complex scenarios. Hence, AI technology is likely to encourage the growth of the Drug Discovery and Clinical Trials Market.

Market Challenges

Lack of Skilled Personnel to Hamper the Market Growth

One of the most prominent challenges in the drug discovery phase is patient health. Dynamic activities interpret information about the documented effects of drugs and anticipate their side effects, which might be a major market restraint. Besides, lack of skilled staff and inadequate datasets for drug discovery & developments are other crucial factors likely to impact the market growth.

Key Topics Covered:

1. Start-up Ecosystem

2. Impact of COVID-19 on Global Artificial Intelligence (AI) Enabled Drug Discovery and Clinical Trials Market

3. Expert Verbatim - Interview Excerpts of 10 industry experts

4. Global Artificial Intelligence (AI) Enabled Drug Discovery and Clinical Trials Market Outlook, 2016-2026F

5. North America Artificial Intelligence (AI) Enabled Drug Discovery and Clinical Trials Market Outlook, 2016-2026F

5.1. Market Size & Analysis

5.2. Market Share & Analysis

5.3. The US Artificial Intelligence (AI) Enabled Drug Discovery and Clinical Trials Market Outlook, 2016-2026F

5.4. Canada Artificial Intelligence (AI) Enabled Drug Discovery and Clinical Trials Market Outlook, 2016-2026F

5.5. Mexico Artificial Intelligence (AI) Enabled Drug Discovery and Clinical Trials Market Outlook, 2016-2026F

6. South America Artificial Intelligence (AI) Enabled Drug Discovery and Clinical Trials Market Outlook, 2016-2026F

6.1. Market Size & Analysis

6.2. Market Share & Analysis

6.3. Brazil Artificial Intelligence (AI) Enabled Drug Discovery and Clinical Trials Market Outlook, 2016-2026F

6.4. Argentina Artificial Intelligence (AI) Enabled Drug Discovery and Clinical Trials Market Outlook, 2016-2026F

7. Europe Artificial Intelligence (AI) Enabled Drug Discovery and Clinical Trials Market Outlook, 2016-2026F

7.1. Market Size & Analysis

7.2. Market Share & Analysis

7.3. UK Artificial Intelligence (AI) Enabled Drug Discovery and Clinical Trials Market Outlook, 2016-2026F

7.4. Germany Artificial Intelligence (AI) Enabled Drug Discovery and Clinical Trials Market Outlook, 2016-2026F

7.5. France Artificial Intelligence (AI) Enabled Drug Discovery and Clinical Trials Market Outlook, 2016-2026F

7.6. Italy Artificial Intelligence (AI) Enabled Drug Discovery and Clinical Trials Market Outlook, 2016-2026F

7.5. Spain Artificial Intelligence (AI) Enabled Drug Discovery and Clinical Trials Market Outlook, 2016-2026F

8. Asia-Pacific Artificial Intelligence (AI) Enabled Drug Discovery and Clinical Trials Market Outlook, 2016-2026F

9. China Artificial Intelligence (AI) Enabled Drug Discovery and Clinical Trials Market Outlook, 2016-2026F

10. India Artificial Intelligence (AI) Enabled Drug Discovery and Clinical Trials Market Outlook, 2016-2026F

11. Japan Artificial Intelligence (AI) Enabled Drug Discovery and Clinical Trials Market Outlook, 2016-2026F

12. Australia Artificial Intelligence (AI) Enabled Drug Discovery and Clinical Trials Market Outlook, 2016-2026F

13. South Korea Artificial Intelligence (AI) Enabled Drug Discovery and Clinical Trials Market Outlook, 2016-2026F

14. Middle East and Africa Artificial Intelligence (AI) Enabled Drug Discovery and Clinical Trials Market Outlook, 2016-2026F

15. UAE Artificial Intelligence (AI) Enabled Drug Discovery and Clinical Trials Market Outlook, 2016-2026F

16. Saudi Arabia Artificial Intelligence (AI) Enabled Drug Discovery and Clinical Trials Market Outlook, 2016-2026F

17. Global Artificial Intelligence (AI) Enabled Drug Discovery and Clinical Trials Dynamics

18. Global Artificial Intelligence (AI) Enabled Drug Discovery and Clinical Trials Regulations, Product Standards

19. Global Artificial Intelligence (AI) Enabled Drug Discovery and Clinical Trials Value Chain Analysis

20. Global Artificial Intelligence (AI) Enabled Drug Discovery and Clinical Trials Intellectual Property Rights Analysis

21. Global Artificial Intelligence (AI) Enabled Drug Discovery and Clinical Trials Hotspots & Opportunities

22. Competition Outlook

For more information about this report visit https://www.researchandmarkets.com/r/v7o7z2

View source version on businesswire.com: https://www.businesswire.com/news/home/20211007005487/en/

Contacts

ResearchAndMarkets.comLaura Wood, Senior Press Managerpress@researchandmarkets.com For E.S.T Office Hours Call 1-917-300-0470For U.S./CAN Toll Free Call 1-800-526-8630For GMT Office Hours Call +353-1-416-8900

Excerpt from:

Global Artificial Intelligence (AI) Enabled Drug Discovery and Clinical Trials Market Report (2021-2026) with COVID-19 Impact and Interview Excerpts...

W&M Professor of Computer Science Gang Zhou (left) and W&M Ph.D. candidate Ken Koltermann are part of a research team developing an innovative new treatment for the movement-related symptoms of Parkinsons disease, which affects more than 10 million people worldwide. Photo by Adrienne Berard

by Adrienne Berard | October 7, 2021

In science there is a term called ground truth, the baseline from which data is judged for accuracy. For William & Mary student Ken Koltermann, the term may better be described as boots-on-the-ground truth.

The third-year Ph.D. candidate in computer science spent five months of the pandemic walking nearly two hours at a time to collect data on his gait data that a team of researchers from W&M and Virginia Commonwealth University are using to develop a novel treatment for a potentially dangerous symptom of Parkinsons disease called Freezing of Gait (or FoG) the temporary inability to move while walking.

I volunteered for it, Koltermann said. I thought, if I'm leading the data collection for this project, I have to do it myself.

It was hard work and it was personal.

My late grandmother actually suffered from Parkinson's and her cases were extremely bad, he said. She had Freezing of Gait episodes where she would have frequent falls, which, as you get older, become more and more serious. It gets easier to break bones, to suffer real injury. Shes the number one reason I got involved in this research.

There is currently no cure for Parkinsons disease, Koltermann explained, only treatments like medications and a surgery procedure called deep brain stimulation, which only helps to suppress some of the symptoms rather than cure the disease.

Koltermann is part of an interdisciplinary research team made up of fellow W&M graduate students Woosub Jung and Minglong Sun, W&M Professor of Computer Science Gang Zhou, Ingrid Pretzer-Aboff, associate professor and senior nurse researcher at the VCU School of Nursing, and Leslie Cloud, associate professor of neurology at the VCU School of Medicine and director of VCUs Parkinsons Disease Program.

The team is developing an innovative new treatment for the movement-related symptoms of Parkinsons disease, which affects more than 10 million people worldwide. The treatment uses a combination of hardware and software to administer targeted vibrations in reaction to certain Parkinsons symptoms, such as tremors and FoG.

Within this team, we have two devices, one developed by Resonate Forward, LLC, and the other co-developed by W&M computer scientists, Zhou said. Were using our combined expertise to design software to communicate with the hardware and develop an effective treatment. Its a totally interdisciplinary project.

The project was recently awarded funding from the National Institute of Neurological Disorders and Stroke at the National Institutes of Health. Zhou explained that the research is filling a critical gap in the way Parkinsons disease is understood and treated. Freezing of Gait is a common yet devastating manifestation of the disease, he said, and its a symptom of the disease for which there is no cure or adequate medical or surgical treatment.

Freezing of Gait can be very isolating socially. Its very burdensome, or at least taxing, on family members that have to help their loved ones. It affects their psyche through the fear of falling, said Pretzer-Aboff, who has studied vibration therapy in Parkinsons patients for more than a decade. There are very few choices out there to help patients like this. Were hoping that using vibration in this new way will show better results and give people some hope, a little more autonomy, freedom and a better quality of life.

FoG episodes are frequently triggered by environmental and psychological factors, such as starting to walk, walking through tight quarters, changing directions, approaching a visual target and dual tasking, she explained. Stressful, time-sensitive situations, such as entering an elevator before the doors begin to close, can also trigger a FoG event.

Each individual with Parkinsons disease is unique with regard to how sensitive they are to these various triggers, underscoring the need for customized therapeutic approaches, the researchers wrote in their grant proposal to NIH. As such, increased understanding of modifiable factors underlying FoG and increased understanding of individual nuances in experiences of FoG is warranted.

The nuances of an individuals gait are not an easy thing to classify. Just ask Koltermann. From August through December of 2020, he regularly traveled up to VCUs physical therapy clinic to collect real-time walking data on himself for the studys baseline, using the universitys Protokinetics ZenoTM Walkway a non-portable, gait-monitoring mat that is currently in widespread clinical use for collecting data on how people walk.

He also wore an UltiGesture band. Its a quarter-sized piece of hardware co-developed by Zhous lab that carries an accelerometer and gyroscope that collect data, which is transmitted to a smartphone via Bluetooth. Artificial intelligence, also developed by Zhous lab, is embedded in an app on the smartphone, which analyzes the data to detect a baseline gait and any abnormalities in it.

I was walking for upwards of 90 minutes at a time with our device mounted on my ankles, just collecting data and comparing it to what we got from the mat, Koltermann explained.

As it turned out, the data he collected was more than enough for the team to be able to begin classifying the various elements of an individuals gait. Once he had collected enough data on his own gait, Koltermann turned to artificial intelligence to take the next steps. He and the team developed a type of machine learning to classify how he walked.

Once that was done, we could confirm that we could accurately detect my gait with our device, Koltermann said.

Zhou explained that validating the hardware and software was a huge milestone. The teams UltiGesture device is highly portable and costs about $10. The gait-detection AI they developed can operate on smartphones, which are also highly portable and widely available. Compare that to currently available gait mats, which are only accessible in clinical or research environments and costs upwards of $50,000.

If we can show our device is as accurate as the mat, that could lead to a revolution in healthcare accessibility and not just for Parkinsons, Koltermann said. This will make gait analysis so much more accessible, especially for rural healthcare systems and low-income areas.

The next phase in the research is to pair the UltiGesture system with the VibeForward device, a small piece of hardware developed by Resonate Forward, LLC, that delivers localized vibrations to the ankle and foot to prompt movement in those experiencing trouble walking or stabilizing themselves.

This technology has great potential to give patients who have Freezing of Gait their independence back, Brian Berman, director of VCUs Parkinsons and Movement Disorders Center, said in a press release. Our researchers continue to strive to find ways to significantly improve the lives of those with Parkinsons disease until a cure can be found.

Together, the UltiGesture and VibeForward device will eventually be able to sense the slightest hint of Freezing of Gait in a patients walk and trigger the ankle device to vibrate within a second to help the wearer stay in motion as usual.

The current treatments available for Parkinson's disease reduce symptoms, but they don't remove the real-time danger of falling from Freezing of Gait, Zhou explained. This system will be able to differentiate between intentional stopping and the involuntary stopping from Freezing of Gait, and then it will be able to provide instant vibration treatment.

Theres also another element of the research, which Zhou says is still in very early stages, but could open up an entirely new realm of possibilities. His team plans to integrate environmental stimuli into data fed to the artificial intelligence, making it possible to sense possible triggers for Freezing of Gait, such as doorways and elevators.

Its really about providing Parkinsons patients a new level of freedom to navigate the world safely, Zhou said. With our environment-dependent classification framework, they will be free to navigate potentially triggering stimuli with far less risk of injury. We are marking the environment for them, adjusting our algorithm to reconfigure and deliver the right dose of the vibration based on that environment. Giving patients that element of freedom, thats a motivating factor here.

View post:

Interdisciplinary research team treating Parkinson's symptoms with artificial intelligence - William & Mary News

Credit: PopTika/Shutterstock

The proportion of automotive manufacturing and supply companies hiring for artificial intelligence related positions rose to a year-high in August 2021, with 38.3% of the companies included in our analysis recruiting for at least one such position.

This latest figure was higher than the 37.7% of companies who were hiring for artificial intelligence related jobs in July 2021 and an increase compared to the figure of 18.3% for the equivalent month last year.

When it came to the proportion of all job openings that were linked to artificial intelligence, related job postings rose in August 2021, with 1.6% of newly posted job advertisements being linked to the topic.

This latest figure was the highest monthly figure recorded in the past year and is an increase compared to the 0.5% of newly advertised jobs that were linked to artificial intelligence in the equivlent month a year ago.

Artificial intelligence is one of the topics that GlobalData, from whom our data for this article is taken, have identified as being a key disruptive force facing companies in the coming years. Companies that excel and invest in these areas now are thought to be better prepared for the future business landscape and better equipped to survive unforseen challenges.

Our analysis of the data shows that automotive manufacturing and supply companies are currently hiring for artificial intelligence jobs at a rate equal to the average for all companies within GlobalData's job analytics database. The average among all companies stood at 1.6% in August 2021.

GlobalData's job analytics database tracks the daily hiring patterns of thousands of companies across the world, drawing in jobs as they're posted and tagging them with additional layers of data on everything from the seniority of each position to whether a job is linked to wider industry trends.

GlobalData exists to help businesses decode the future to profit from faster, more informed decisions.

28 Aug 2020

GlobalData can provide actionable insights to drive your company forward

28 Aug 2020

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Artificial intelligence hiring levels in the automotive industry rose to a year-high in August 2021 - just-auto.com