Category Archives: Artificial Intelligence

NIH has issued a challenge to develop an algorithm that can identify COVID-19 patients with a high risk of developing Long COVID.

NIH has issued a challenge to develop an algorithm that can identify COVID-19 patients with a high risk of developing Long COVID.

Studies have shown that recovery from infection with SARS-CoV-2, the virus that causes COVID-19, can vary from person to person. Most patients seem to recover from COVID-19 quickly and completely. However, others report experiencing COVID-19 symptoms that last for weeks or months or developing new symptoms weeks after infection. These long-term effects are called post-acute sequelae of SARS-CoV-2 (PASC) or, more commonly, Long COVID.

To better understand which patients develop Long COVID, the NIH Rapid Acceleration of Diagnostics (RADx) initiative has launched the NIH Long COVID Computational Challenge (L3C). Part of the RADx-Radical program, the challenge aims to support research that uses artificial intelligence and machine learning tools to identify COVID-19 patients with a high risk of developing Long COVID. The challenge will award $500,000 in total cash prizes to first, second, and third place and up to five honorable mentions.

Researchers who participate in the challenge will create algorithms that analyze anonymized medical records to find out which patients with SARS-CoV-2 infections are most likely to develop Long COVID.

A panel of judges will test the projects on quantitative metrics, such as how well the projects can analyze data from different times, sites, and demographics. The judges will also test the projects on qualitative metrics, such as whether the tool can predict Long COVID risk before the patient is diagnosed in a different way and how likely health care providers would be to use the tool in their clinic.

The submission deadline is December 15, 2022. Winners will be announced in March 2023.

The tools developed for this challenge can help health care providers predict whether a person infected with SARS-CoV-2 has a high risk for having Long COVID later. If providers could identify people at high risk of Long COVID, they would have a chance to recommend ways to manage symptoms and prevent that outcome.

NIH Long COVID Computational Challenge (L3C)

Researching COVID to Enhance Recovery (RECOVER) Initiative

Studying Long COVID Might Help Others With Post-Viral Fatigue Ailments

Avindra Nath, M.D., the clinical director of the National Institute of Neurological Disorders and Stroke, discusses Long COVID research and how it can benefit people with other diseases.

See the rest here:

Predicting Long COVID with Artificial Intelligence - National Institutes of Health (NIH)

Created by robots for humans is a special edition body spray for young men which has been developed using augmented reality and artificial intelligence in a bid to produce the perfect scent and packaging.

Axe A.I. (LYNX A.I. in the UK), is the result of a specially designed AI applied to analyze 6,000 perfume ingredients with 3.5million potential combinations with the goal of discovering the ideal fragrance. The result combines an aromatic floral blend of sage, artemisia, and mint, refreshed with marine, apple and citrus notes and finished with a woody, ambery, and moss background.

Not only did the brand use AI algorithms to help create the scent but they are doubling down on technology and are using Augmented Reality (AR) to help market it.

Powered by Zappars WebAR technology, all limited edition Lynx/Axe A.I. packs will feature a smartphone scannable QR code that will launch a web page where British rapper Aitch will introduce the product and ask the user to spray an AR can of LYNX/Axe in the air to reveal a code allowing entry in a special competition. Six lucky winners will be invited to a special house memorable party hosted by a hologram of Aitch himself.

Senior brand manager for Lynx at Unilever, Josh Plimmer, said: Lynx has always been at the -cutting-edge of fragrance. The launch of Lynx A.I. which was created by crunching 46 terabytes of data, unlocks the code to smell iconic. This groundbreaking new scent was created in collaboration with Swiss firm Firmenich, which is an expert in fragrance and taste.

Caspar Thykier, CEO and co-founder at Zappar added: The Lynx AI concept and campaign clearly demonstrate how innovative brands are leveraging technology to create and market new products, getting ahead in the new connected pack revolution as an always-on platform and part of their owned-media strategy. AR and AI are driving more engaging product experiences and better connecting young customers with brands.

It is daring, courageous and innovative to combine humans and technology in such an emotional field - the sense of smell. commented Firmenichs chief information officer, Eric Saracchi, We are very excited to launch this game-changing fragrance with Lynx to power up guys daily routines. Over 50 years of fragrance data and knowledge - along with over 46 terabytes of data has led to the creation of Lynx A.I.

The special edition Lynx/Axe A.I. campaign will run through to March 2023.

This article was created in collaboration with AIPIA (the Active and Intelligent Packaging Industry Association). Packaging Europe and AIPIA are joining forces to bring news and commentary about the active and intelligent packaging landscape to a larger audience. To learn more about this partnership, click here.

Here is the original post:

Artificial intelligence and augmented reality freshen up men's fragrance - Packaging Europe

Opinion: AI can now help write essays that appear highly similar, if not indistinguishable, from that of a human author

By Eamon Costello and Mark Brown, DCU

Artificial intelligence-powered automatic essay writing technologies have taken a huge leap forward and are becoming widely available. In many cases, AI can help write essays that appear highly similar, if not indistinguishable, from that of a human author.

As this piece was being typed, Google AI helpfully suggested words to auto-complete these sentences. This speeds up writing but other tools and services, such as those based on the GPT-3 technology from the OpenAI group, can do much more than simple sentence completion: they can write the whole thing from scratch from start to finish.

AIs can generate surprising comprehensible text

By simply taking a prompt of a sentence or phrase from a human author, AIs can generate surprising comprehensible text. When the prompt "when an AI wrote my essay" was typed into a freely available online AI text generator to see what it would produce, the AI wrote: the following

"And don't forget", the professor said, "AIs are not allowed to write your essay or help you write your essay in any way" I groaned and rolled my eyes, thinking that I knew what he was going to say. I had never been good at writing essays, and I knew that an AI would only make it worse. But when I sat down to start it, I found myself in the middle of a beautiful essay. The words flowed easily from my fingers, and I was suddenly inspired. I had never written so well, and I was surprised that the AI had written it for me. I finished the essay and handed it in, feeling proud of myself. I didn't know what the professor would say, but I was sure that I had done a better job than if I had written it myself.

One can see how a student could generate text in this way, perhaps generating several texts, seeing which one suited best and then lightly editing for clarity. AI can also take large bodies of text as input and attempt to summarize them.

We need your consent to load this rte-player contentWe use rte-player to manage extra content that can set cookies on your device and collect data about your activity. Please review their details and accept them to load the content.Manage Preferences

From RT Radio 1's Today with Claire Byrne, Brian O'Connell reports on student essay mills

A student could prompt an AI to write several paragraphs and could then select which paragraphs seemed most well written and coherent. Finally, they could feed these paragraphs back to the AI asking it to summarise them. This could form the basis of a summary/conclusion section of an essay.

Using AI in this way requires particular skills. Just as correct spelling is becoming less critical with the ability of autocorrect, perhaps essay writing will evolve similarly. It may be that writers in the future engage in the higher level activity of orchestrating a composition, while AI does the heavy lifting of producing the actual sentences.

If using a spell checker isn't seen as cheating, will the use of AI for essay writing then be accepted as the new normal? Perhaps future students will use AIs to write their essays, while professors deploy AIs to check their authenticity.

Of course AIs can grade essays too, but does this mean that teachers will have less work?

Of course AIs can grade essays too, but does this mean that teachers will have less work? The jury is still out on this question: one major review of the research on AI in education found a conspicuous absence of reference to actual teachers. One scenario is a teacherless future where students are accelerated through courses of study by advanced robo-Profs.

A contrasting future has been foreseen by AI education expert Peirre Dillenbourg. He has predicted that we will have more teachers in the future, not less. He foresees teachers working in teams to oversee and design learning scenarios using multiple AIs dedicated to specific educational tasks.

That is the future taken care of but what about the present? Universities worldwide currently invest heavily in anti-plagiarism and academic integrity technologies. Many of these systems have been termed 'data-extractive', in that they often rely on extracting and mining large bodies of student work. At their worst these expensive systems can create climates of fear., where students feel they are being policed by big brother or sister.

With all of the fuss about AI, it is worth remembering that people are always at the heart of education

AI essay writing may be seen as just another chapter in the long history of so-called "essay mills", services that students can use to commission and buy their homework from. Will AI make these services redundant in the future? What constitutes cheating and breaches of academic integrity in the world of AI? After all, irrespective of how we define cheating, who loses if the student does not fully engage in their own learning?

Something that educators can do is to have conversations with students about their learning and especially their assessment. A guiding principle should be that a student will always want to do the work themselves given the right conditions. This is the opposite of a starting principle that says: every student is a potential cheater.

Assessment mixes that are not completely dependent on traditional essays can allow students to express themselves in a variety of ways. Do we try to tame AI to protect old ways of learning or should we embrace its potential and reimagine our assessment practices to reflect the modern reality of living in the 21st century? One creative educator had his students purposefully use and evaluate AI essay writers as part of their assignment.

With all of the fuss about AI, it is worth remembering that people are always at the heart of education. Student and teacher workloads should be key considerations in the design of assessment. Giving each other space to build trusting environments in which to teach and learn will require much human ingenuity, care and intelligence.

Dr Eamon Costello is an Associate Professor of Digital Learning at the DCU Institute of Education. Professor Mark Brown is Chair of Digital Learning and Director of the National Institute for Digital Learning at DCU.

The views expressed here are those of the author and do not represent or reflect the views of RT

Read the original post:

Will artificial intelligence be able to write my college essay? - RTE.ie

The software developed in cooperation between Semmelweis University (SE), Ulyssys Kft., and the Institute for Computer Science and Control (SZTAKI), which uses artificial intelligence to automatically analyze CT images (Computed Tomography) and aids the early detection of cancer, could be a milestone in lung cancer diagnostics, said Bla Merkely, Rector of SE.

The rector also said that the cutting-edge development will enable the automatic and rapid diagnostic analysis of chest CT scans, thereby reducing the burden on doctors.

Bla Merkely stressed that education, research innovation, and medicine have helped the university to become an internationally recognized center of excellence, making it the most successful institution in higher education rankings in Central Europe. Their aim is to rank among the top 100 universities in the world and among the top five medical universities in Europe.

Bla Merkely, Rector of Semmelweis University; MTI/Zoltn Balogh

Pter Wellisch, Managing Director of Ulyssys IT Services and IT Consulting, said that good cooperation between the university research sector and the SME sector had been successfully established. He said that they had been working on artificial intelligence for a long time, but had not previously thought that healthcare would be one of the areas where it could be applied.

Asked when he expects the program to be used in Hungarian patient care, Wellisch said a clinical trial is under way and will be completed in a few months. He hopes to have the program in place in the second half of next year.

Pl Maurovich Horvat, director of the Medical Imaging Center at Semmelweis University, said lung cancer was also one of the leading causes of death in Hungary, which had the highest mortality rate in Europe. He also said that the five-year survival rate of lung cancer is greatly influenced by when a tumor is detected.

The project was part of the Szchenyi 2020 program, for which the Hungarian government provided HUF 1.448 billion (35.7 mil. EUR) in EU funding.

Via: MTI; Featured image: Pixabay

Read the original:

Fighting Cancer with Artificial Intelligence - Hungary Today

The lockdown generated a wave of disruption across the retail industry, leaving many physical retailers scrambling to meet public demand. They had to pivot their focus on creating highly optimized digital shopping experiences and supply chains to compete against the e-commerce giants. This period of heightened competition led retailers to focus a disproportionate amount of their innovation budgets on refining their online shopping experiences. Now that footfall levels at their brick & mortar stores have recovered to pre-pandemic levels, we are seeing that the customer returning to the store is finding a large variance in the shopping experience between the brands physical and online shopping channels. In an era of commoditization, the consumer has made it clear that the in-person shopping experience of engaging with a brands physical footprint is still critical for them to remain loyal to the brand. However, retailers will struggle to retain their customer base unless they start delivering an equally frictionless shopping experience in their stores. To catalyze this journey of transformation, retail must embrace the benefits of data-driven artificial intelligence (AI).

Unlocking in-store analytics data with computer vision

To deliver an optimized in-store shopping experience, retailers need to radically transform the way they operate their stores. To do this effectively, they need to adopt a data-driven approach where they micro analyze the customer conversion funnel to learn what bottlenecks are present in each of their stores. The only way to do this in a scalable manner is by adopting AI. There are several AI tools that an organization can adopt, but the most promising one that can deliver near term value for retailers is computer vision. This field of computer science focuses on replicating parts of the human vision system by enabling computers to identify and process objects, images, and videos in the same way that humans do. Vendors use a combination of proprietary algorithms and a real-time inference pipeline with deep learning to identify objects in a physical space and analyze their behavior and movement to predict outcomes and drive success. With the AI engine continuously analyzing these variables, it empowers both store managers and executives with insights on how they can make their retail business more efficient and profitable.

Decentralized data-drive decision making

Large retailers have struggled to objectively assist their store managers with granular insights on how they should manage each of their stores. This happens as executives drive most of their strategic decisions on a centralized level based on one dimensional data set such as point-of-sale and loyalty data. However, now with computer vision the retailers can get access to a much deeper set of shopper insights. By leveraging the video footage from their existing CCTV cameras, they can measure store visitor counts, traffic flow patterns throughout the store, segment visitors by their shopping duration, time of day and even demographics. The AI engine then analyses which of these variables corelate to maximized conversion rates and then provides suggestions on how operators should optimize everything from merchandising, store layouts, operations, and labor scheduling.

To compete with the online shopping experience effectively, retailers can longer afford to implement regional or district level strategies. They need to understand the interplay of these variables on a micro store level. Once they have visibility of what is and what isnt working in each of their stores, the AI engine will provide a combination of real-time and prescriptive analytics on how each store needs to be run. The engine will continue to consistently A/B test store level strategies and will empower every store manager by simplifying their store operations and providing an enhanced in-store shopping experience.

Embracing AI to sweat your physical assets

With the continued growth of e-commerce, retailers are sitting at a crossroad where they need to decide if they will skew their balance sheet towards their online of offline retail strategies. The retailers that continue to operate their stores as they have in the past will face a slow death as they lose the loyalty of their customers. On the other hand, operators that adopt a culture of agile innovation for their physical assets in a similar manner to their online strategies will emerge as the dominant players in the retail landscape. Customers continue to remain loyal only to convenience and the brands that deliver the most frictionless shopping experience by leveraging tools such as computer vision will be able to effectively maximize the value of every square footage of their brick-and-mortar footprints.

Views expressed above are the author's own.

END OF ARTICLE

Original post:

How computer vision and artificial intelligence can help the retail industry maximize the value of their brick - Times of India

DUBLIN, Sept. 30, 2022 /PRNewswire/ -- The "Artificial Intelligence Chip Market by Chip Type, by Application, by Architecture, by Processing Type, by End User - Global Opportunity Analysis and Industry Forecast, 2022-2030" report has been added to ResearchAndMarkets.com's offering.

The Global Artificial Intelligence (AI) Chip Market size was valued to USD 20.77 billion in 2021, and it will elevate to USD 304.09 billion by 2030, with a CAGR of 29.9% from 2022-2030.

Artificial Intelligence Chips are special silicon chips, programmed for machine learning. AI Chips can process vast amount of data, identify the underlying patterns, interpret the trends and utilize the feed to achieve specific goals. AI Chips are multi-functioning and can proficiently manage multiple operations at a time.

The demand for AI Chip is consistently raising due to the increasing adoption of artificial intelligence in almost every industry, in areas such as voice recognition, object detection, medical or military simulation, intelligent routing, and autonomous driving among others. Adoption of artificial intelligence not only reduces the cost of operations, but increases the efficiency or the response time and minimizes the risk to human life at various levels in specific industry verticals.

Market Dynamics and Trends

With the technological advancements the market is switching towards smart devices, smart homes, and smart cities, resulting to a tremendous elevation in the Artificial Intelligence (AI) Chip market. Furthermore, increased investments in AI start-ups along with emergence of quantum computers, are expected to elevate the market growth in future.

Other factors like wide application of AI technology and increase in robotics, catalyze to promote the market growth. However, high development cost and lack of skilled work-force tend to impede the growth of artificial intelligence (AI) chip market over the forecast period.

Moreover, the massive upsurge in research and development, increased use of autonomous robotic at various industry verticals, and high-tech product launches shall create new market-opportunities, fueling-up the growth-rate of artificial intelligence (AI) chip market, over the forecast period.

Market Segmentations and Scope of the Study:

The global artificial intelligence (AI) chip market share analysis is based on chip type, application, architecture, processing type, end user and geography. Based on type of chip, the market is fragmented into GPU, FPGA, ASIC, CPU, and Others. Based on application, the market is segmented into Natural Language Processing (NLP), Robotic Process Automation, Computer Vision, Network Security, and Others.

Based on architecture, the market is segmented into system-on-chip (soc), system-in-package (sip), multi-chip module, and others. Based on processing type, the market is divided into edge and cloud. Based on end-user, the market is segmented into media & advertising, BFSI, IT & telecom, retail, healthcare, automotive, and others. Geographic breakdown and analysis of each of the previously mentioned segments include regions comprising North America, Europe, Asia-Pacific, and RoW.

Geographical Analysis

North America represents the higher adoption of the artificial intelligence chip technology, henceforth it is expected to hold the highest market share in the global Artificial intelligence (AI) chip market during the forecast period. This is attributable to the factors like; massive use of AI technology, increased use of smart devices, and wide application of AI chip in various industries.

The emerging economies, specifically the Asia-Pacific region shall witness an increasing market size in the global Artificial intelligence (AI) chip market, owing to the amplified adoption of AI technology and increased investment in the AI start-ups.

Competitive Landscape

The artificial intelligence (AI) chip market is highly competitive and consists numerous market players. Some of the major market players are, Advanced Micro Devices, Inc., IBM Corporation, Micron technology, Inc., Qualcomm Incorporated, Xilinx, Inc., Alphabet Inc. (Google), Intel Corporation, NVIDIA Corporation, Samsung electronics Co., Ltd., and Huawei Technologies Co., Ltd. among others.

There have been various developments taking place in the market that further enhance the growth of artificial intelligence (AI) chip market on a large scale. For instance, in September 2019, ZTE, a provider of integrated military communication solutions from Cambrian, jointly demonstrated the integration of edge computing and artificial intelligence in the 5G era. The business display adopts ZTE's edge computing server ES600S, video acceleration card, and contains the Cambrian Siyuan 100 intelligent processing card which not only helps in realizing the edge video data collection, but also provides the artificial intelligence analysis.

Key Topics Covered:

1. Introduction

2. Artificial Intelligence (AI) Market Executive Summary

3. Market Overview3.1. Market Definition and Scope3.2. Market Dynamics3.2.1 Drivers3.2.1.1. Growing Adoption of Deep Learning and Neural Networks3.2.1.2. Increase in Demand for Smart Homes & Smart Cities3.2.1.3. Emergence of Quantum Computing3.2.1.4. Rising Use of Adas Features in Cars3.2.2 Restraints3.2.2.1. Lack of Skilled AI Workforce3.2.2.2. Limited Structured Data3.2.3 Opportunities3.2.3.1. Increasing Focus on Human-Aware AI Systems and Development of Smarter Robots

4. Global AI Chip Market, by Chip Type4.1. Overview4.2. GPU4.3. GPU Market, by Region4.4. ASIC4.4.1 ASIC Market, by Region4.5. FPGA4.5.1 FPGA Market, by Region4.6. CPU4.6.1 CPU Market, by Region4.7. Others4.7.1 Others Market, by Region

5. Global AI Chip Market, by Application5.1. Overview5.2. Natural Language Processing (Nlp)5.2.1 Natural Language Processing (Nlp) Market, by Region5.3. Robotic Process Automation5.3.1 Robotic Process Automation Market, by Region5.4. Computer Vision5.4.1 Computer Vision Market, by Region5.5. Network Security5.5.1 Network Security Market, by Region5.6. Others5.6.1 Others Market, by Region

6. Global AI Chip Market, by Architecture6.1. Overview6.2. System on Chip (Soc)6.2.1 System on Chip (Soc) Market, by Region6.3. System in Package (Sip)6.3.1 System in Package (Sip) Market, by Region6.4. Multi Chip Module6.4.1 Multi Chip Module Market, by Region6.5. Others6.5.1 Others Market, by Region

7. Global AI Chip Market, by Processing Type7.1. Overview7.2. Edge7.2.1 Edge Market, by Region7.3. Cloud7.3.1 Cloud Market, by Region

8. Global AI Chip Market, by End-User Industry8.1. Overview8.2. Media and Advertising8.2.1 Media and Advertising Market, by Region8.3. Bfsi8.3.1 Bfsi Market, by Region8.4. It and Telecommunication8.4.1 It and Telecommunication Market, by Region8.5. Retail8.5.1 Retail Market, by Region8.6. Healthcare8.6.1 Healthcare Market, by Region8.7. Automotive8.7.1 Automotive Market, by Region8.8. Others8.8.1 Others Market, by Region

9. Global AI Chip by Region

10. Company Profiles10.1. Alphabet Inc.10.1.1 Company Overview10.1.2 Company Snapshot10.1.3 Operating Business Segments10.1.4 Product Portfolio10.1.5 Business Performance10.1.6 Sales by Business Segment10.1.7 Sales by Geographic Segment10.1.8 Key Strategic Moves & Developments10.1.9 Primary Market Competitors10.2. Apple Inc.10.2.1 Company Overview10.2.2 Company Snapshot10.2.3 Operating Business Segments10.2.4 Product Portfolio10.2.5 Business Performance10.2.6 Sales by Business Segment10.2.7 Sales by Geographic Segment10.2.8 Key Strategic Moves & Developments10.2.9 Primary Market Competitors10.3. Arm Holdings10.3.1 Company Overview10.3.2 Company Snapshot10.3.3 Product Portfolio10.3.4 Key Strategic Moves & Developments10.3.5 Primary Market Competitors10.4. Huawei Technologies10.4.1 Company Overview10.4.2 Company Snapshot10.4.3 Operating Business Segments10.4.4 Product Portfolio10.4.5 Business Performance10.4.6 Sales by Business Segment10.4.7 Sales by Geographic Segment10.4.8 Key Strategic Moves & Developments10.4.9 Primary Market Competitors10.5. IBM Corporation10.5.1 Company Overview10.5.2 Company Snapshot10.5.3 Operating Business Segments10.5.4 Product Portfolio10.5.5 Business Performance10.5.6 Sales by Business Segment10.5.7 Sales by Geographic Segment10.5.8 Key Strategic Moves & Developments10.5.9 Primary Market Competitors10.6. Samsung Electronics10.6.1 Company Overview10.6.2 Company Snapshot10.6.3 Operating Business Segments10.6.4 Product Portfolio10.6.5 Business Performance10.6.6 Sales by Business Segment10.6.7 Sales by Geographic Segment10.6.8 Key Strategic Moves & Developments10.6.9 Primary Market Competitors10.7. Qualcomm Inc10.7.1 Company Overview10.7.2 Company Snapshot10.7.3 Operating Business Segments10.7.4 Product Portfolio10.7.5 Business Performance10.7.6 Sales by Business Segment10.7.7 Sales by Geographic Segment10.7.8 Key Strategic Moves & Developments10.7.9 Primary Market Competitors10.8. Nxp Semiconductors10.8.1 Company Overview10.8.2 Company Snapshot10.8.3 Operating Business Segments10.8.4 Product Portfolio10.8.5 Business Performance10.8.6 Sales by Business Segment10.8.7 Sales by Geographic Segment10.8.8 Key Strategic Moves & Developments10.8.9 Primary Market Competitors10.9. Nvidia Corporation10.9.1 Company Overview10.9.2 Company Snapshot10.9.3 Operating Business Segments10.9.4 Product Portfolio10.9.5 Business Performance10.9.6 Sales by Business Segment10.9.7 Sales by Geographic Segment10.9.8 Key Strategic Moves & Developments10.9.9 Primary Market Competitors10.10. Microsoft Corporation10.10.1 Company Overview10.10.2 Company Snapshot10.10.3 Operating Business Segments10.10.4 Product Portfolio10.10.5 Business Performance10.10.6 Sales by Business Segment10.10.7 Sales by Geographic Segment10.10.8 Key Strategic Moves & Developments10.10.9 Primary Market Competitors10.11. Microsemi Corporation10.11.1 Company Overview10.11.2 Company Snapshot10.11.3 Operating Business Segments10.11.4 Product Portfolio10.11.5 Business Performance10.11.6 Sales by Business Segment10.11.7 Key Strategic Moves & Developments10.11.8 Primary Market Competitors10.12. Micron Technology10.12.1 Company Overview10.12.2 Company Snapshot10.12.3 Operating Business Segments10.12.4 Product Portfolio10.12.5 Business Performance10.12.6 Sales by Business Segment10.12.7 Sales by Geographic Segment10.12.8 Key Strategic Moves & Developments10.12.9 Primary Market Competitors10.13. Mediatek Inc10.13.1 Company Overview10.13.2 Company Snapshot10.13.3 Product Portfolio10.13.4 Business Performance10.13.5 Sales by Business Segment10.13.6 Key Strategic Moves & Developments10.13.7 Primary Market Competitors10.14. Intel Corporation10.14.1 Company Overview10.14.2 Company Snapshot10.14.3 Operating Business Segments10.14.4 Product Portfolio10.14.5 Business Performance10.14.6 Sales by Business Segment10.14.7 Sales by Geographic Segment10.14.8 Key Strategic Moves & Developments10.14.9 Primary Market Competitors10.15. Texas Instruments Incorporated10.15.1 Company Overview10.15.2 Company Snapshot10.15.3 Operating Business Segments10.15.4 Product Portfolio10.15.5 Business Performance10.15.6 Sales by Business Segment10.15.7 Sales by Geographic Segment10.15.8 Key Strategic Moves & Developments10.15.9 Primary Market Competitors

For more information about this report visit https://www.researchandmarkets.com/r/1qcmvd

Media Contact:

Research and MarketsLaura Wood, Senior Manager[emailprotected]

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

U.S. Fax: 646-607-1904Fax (outside U.S.): +353-1-481-1716

Logo: https://mma.prnewswire.com/media/539438/Research_and_Markets_Logo.jpg

SOURCE Research and Markets

See the article here:

The Worldwide Artificial Intelligence Chip Industry is Expected to Reach $304 Billion by 2030 - PR Newswire

LONDON, Sept. 27, 2022 (GLOBE NEWSWIRE) -- According to The Business Research Companys research report on the high energy lasers market, the emergence of artificial intelligence is gaining popularity among the high energy lasers industry trends. Many companies operating in the market are focused on developing AI-based products to get a competitive advantage.

For instance, in April 2022, the US Navy successfully tested the Layered Laser Defense (LLD), a laser weapon designed and developed by Lockheed Martin, a US-based aerospace, arms, defense, information security, and technology company. This is the Layered Laser Defense (LLD). It can use a high-power laser to counter unmanned aerial systems and fast-attack boats, as well as track inbound air threats, support combat identification, and conduct battle damage assessments of engaged targets. With specialized optics for viewing a target and directing laser beams for maximum effect, as well as artificial intelligence to improve tracking and aiming, LLD is compact and powerful yet more efficient than previous systems.

Request for a sample of the global high energy lasers market report

The global high energy lasers market size is expected to grow from $10.42 billion in 2021 to $12.05 billion in 2022 at a compound annual growth rate (CAGR) of 15.7%. The global high energy lasers market growth is expected to reach $19.97 billion in 2026 at a CAGR of 13.5%.

The rising demand for laser weapon systems in defense is expected to propel the growth of the high-energy laser market going forward. Laser weapons systems are described as systems that solve recognized capability gaps against asymmetric threats (UAS [unmanned aerial systems], small boats, and ISR sensors). Laser weapon systems are highly necessary in the Navy and Air Force across the world to prevent airborne threats like missiles and drones, as lasers are effective against missiles and are being used as the first line of defense. For instance, in 2019, Northrop Grumman Corporation, a US-based aerospace and defense company, and the United States Air Force Research Laboratory (AFRL), inked a contract to develop an advanced laser system. The laser system has been used to defend US fighter jets against enemy missiles. Apart from that, in 2021, the DRDO (Defense Research and Development Organization), an India-based government agency aimed at the production of a high-power laser weapon, is eyeing a budget of $100 million from the ministry of defense. As a result, the market, which was valued at $7.4 billion in 2020, is expected to reach $14.7 billion by 2026. Therefore, rising demand for laser weapon systems in defense is driving the growth of the high energy market.

Major players in the high energy lasers market are SICK AG, Petasense Inc, Allegro MicroSystems, Inc., Robert Bosch GmbH, NXP Semiconductors NV, Infineon Technologies AG, Sensoronix, Inc., TE Connectivity, Inc., SPECTEC, Sensor Solutions Corporation, ABB, Rockwell Automation Inc., STMicroelectronics, Pepperl+Fuchs, and Schneider Electric.

The global high energy lasers market is segmented by product type into gas laser, fiber laser, solid state laser, excime laser, and by application into cutting, welding, and drilling, military and defence, and communications.

North America was the largest region in the high energy lasers market in 2021. Asia-Pacific is expected to be the fastest-growing region in the high energy lasers market during the forecast period. The regions covered in the global high energy lasers market research report are Asia-Pacific, Western Europe, Eastern Europe, North America, South America, the Middle East, and Africa.

High Energy Lasers Global Market Report 2022 Market Size, Trends, And Global Forecast 2022-2026 is one of a series of new reports from The Business Research Company that provide high energy lasers market overviews, analyze and forecast market size and growth for the whole market, high energy lasers market segments and geographies, high energy lasers market trends, high energy lasers market drivers, high energy lasers market restraints, high energy lasers market leading competitors revenues, profiles and market shares in over 1,000 industry reports, covering over 2,500 market segments and 60 geographies.

The report also gives in-depth analysis of the impact of COVID-19 on the market. The reports draw on 150,000 datasets, extensive secondary research, and exclusive insights from interviews with industry leaders. A highly experienced and expert team of analysts and modelers provides market analysis and forecasts. The reports identify top countries and segments for opportunities and strategies based on market trends and leading competitors approaches.

Not the market you are looking for? Check out some similar market intelligence reports:

Laser Weapon Systems Market 2022 By Product (Laser Designator, Lidar, 3d Laser Scanning, Laser Range Finder, Ring Laser Gyro, Laser Altimeter), By Technology (Solid State Laser, Chemical Laser, Free Electron Laser, Chemical Oxygen Iodine Laser, Tactical High Energy Laser, Others), By Application (Air Based, Ground Based, Sea Based), And By Region, Opportunities And Strategies Global Forecast To 2031

Laser Marking Machine Global Market Report 2022 By Offering (Hardware, Software, Services), By Type (CO2 Laser, Fiber Laser, Green Laser, UV Laser, YAG Laser, Others), By Application (Automotive, Aerospace, Machine Tool, Electronics And Microelectronics, Medical, Packaging, Military, Others) Market Size, Trends, And Global Forecast 2022-2026

Laser Cutting Machine Global Market Report 2022 By Technology (Solid State, Gas, Semiconductor), By Process (Fusion Cutting, Flame Cutting, Sublimation Cutting), By End User Industry (Automotive, Consumer Electronics, Defense And Aerospace, Industrial, Other End User Industries) Market Size, Trends, And Global Forecast 2022-2026

Interested to know more about The Business Research Company?

The Business Research Company is a market intelligence firm that excels in company, market, and consumer research. Located globally it has specialist consultants in a wide range of industries including manufacturing, healthcare, financial services, chemicals, and technology.

The Worlds Most Comprehensive Database

The Business Research Companys flagship product, Global Market Model, is a market intelligence platform covering various macroeconomic indicators and metrics across 60 geographies and 27 industries. The Global Market Model covers multi-layered datasets which help its users assess supply-demand gaps.

Originally posted here:

The Emergence Of Artificial Intelligence Is A Key Trend In The High Energy Lasers Market As Per The Business Research Company's High Energy Lasers...

Turing Prize winner (considered the Nobel Prize in computing) Geoffrey Hinton said a few months ago that artificial intelligence is going to solve everything. But is it so? Is Artificial Intelligence Really Smart?

When we talk about Artificial Intelligence (AI from now on), we should not forget that we are talking about computer program (code) which is designed, developed and maintained by people so that it Let the program influence and influence the people. AI is therefore created by people. It is true that AI allows you to do new things; We relate differently, we communicate differently, etc. but on the bottom The concern is another: that one group of humans has advantage and control over another group of humans., Machines have no emotion, feeling, desire, purpose, or autonomy. However, humans do. And this is where public power must rise to control what is done and what is not.

The areas in which society can obtain intelligence are very broad. They can be described from the point of view of civil service, increased job opportunities, better security, access to public information and transparency etc. And, of course, from a more business point of view. And it is that many sectors of economic activity see opportunity in this data economy. And thats where Hintons phrase we quoted at the beginning makes sense. Is everything solvable? Whats more, do we want it to be able to solve everything?

Till date, Machines have not naturally demonstrated their ability to understand and abstract, It is in fact the basis of human intelligence. This is what sets us apart from other species. So we are currently talking more about extended intelligence than artificial intelligence. In addition, and more importantly, we have problems with interpretability and transparency. When we use neural networks, for example, which are autonomic (and quite opaque in terms of how and what they learn), it is difficult to understand how much weight is given to each variable to predict something. Do we rely on this ambiguity to predict the possible development of diseases? In other words, even if we dont know why an algorithm has learned to prioritize some variables over others, do we rely on it to make decisions?

Perhaps we should start softening these absolutist discourses, and bet on simpler models, which tend to be less successful, but at least we know what and how they predict. This area of AI is called eXplainable AI (XAI). It suggests that if we want an algorithmic society, we need to know which variables are relevant, report confidence intervals, and describe what it has learned. That is, actually understanding the magic that happens is based on looking for patterns within the algorithm.

Otherwise, we would create dark boxes in what Frank Pascal calls the Black Box Society. The book The Black Box Society: The Secret Algorithms that Control Money and Information, by the above author, introduces the ethics of technology from three perspectives that should be considered when we build algorithms: (1) that The resulting result must satisfy a series of rules, policies, principles, etc.; (2) evaluate the results obtained by these algorithms; (3) Include clearly and implicitly the shared values in the society in which they will be introduced.

as you can see, This is not an exercise to regulate technologies. But what can we humans do with it?, There is still no artificial intelligence law in any country. Yes, many agendas and plans. The power of data is clear. Not so much the ethics and the social and inclusive perspective. Whether the power brought by these figures is in our hands or not. And the thing is, AI is more human than artificial. And it is about expanding human capabilities rather than artificially altering them. He

Read more:

Artificial Intelligence But Not Social - Nation World News

Marketing is one of the areas of business operations where it is widely predicted that artificial intelligence (AI) will drive enormous change. In fact, a McKinsey study found that, along with sales, it is the single business function where it will have the most financial impact. This means that if youre a marketer and youre not using AI, youre missing out on the benefits of what is possibly the most transformational technology.

Artificial Intelligence And The Future Of Marketing

Actually, though, the chances that there are people out there doing marketing today and not using AI in any shape or form is somewhat unlikely. This is simply because there are so many tools with AI features that we are used to using without even thinking about it. The most frequently used social and search engine advertising solutions, email marketing platforms, e-commerce solutions, and tools designed to assist with content creation all provide functionality that taps into what we refer to as AI in business today. To be clear, this isnt what we think of as general AI machines that have the capability to think and communicate like us and turn their hands to just about any task. In business today (and in marketing in particular), AI refers to software that helps us to carry out one particular job such as identifying where to place advertising in order to maximize efficiency or how to personalize an email to increase the likelihood of receiving a reply and get better and better as it is exposed to more data.

However, its my experience that, while there may be many tools out there and most marketers are increasingly comfortable with using them on a day-to-day basis, its often done in an ad-hoc manner. Many marketing departments still lack a coordinated, strategy-focused approach to implementing bigger projects. Just as importantly, many are lagging when it comes to fostering an AI-friendly, data-first culture as well as developing competencies and upskilling in order to meet the skills demand.

Paul Roetzer, founder and CEO of Marketing AI Institute and author of the new book Marketing Artificial Intelligence, told me that this is true in his experience too. In fact, when recently setting out to check up on his own hunch by searching for mentions of AI terminology in connection with 50 of the worlds top chief marketing officers, he found that only four of them had spoken publicly or been connected with their use of AI.

My question was, who is leading this? Who is doing this within marketing?

So, what we found was the industries that have a lot of data and a need for heavy personalization, and intelligent automation of their operations have been doing AI for probably the last decade - healthcare, financial services - but doing it within the operations of their business, not within marketing and sales.

But those same industries have a strong need for personalization, better customer experiences, better predictability of outcomes, the reasons youd use AI. But generally, at a macro level, we are extremely early in the understanding and adoption of AI ; that is my perception.

So what are the most exciting opportunities when it comes to using AI in marketing, and where are they already being tapped?

Advertising

Advertisers face the perennial problem of working out how best to place adverts in order to achieve the most bang for their buck.

Facebook and Google are the biggest online advertising platforms, and they both offer tools that work by combining audience segmentation with predictive analytics. Segmentation splits customers into groups according to characteristics gender, age, income level, interests, for example, and potentially an infinity of others. Predictive analytics works out which of these groups a particular product or service is most likely to appeal to. Facebook, Google, and all of the other platforms that offer advertising functions then allow businesses to target thousands of potential customers with multiple different versions of advertising materials in order to measure and assess their effectiveness. With traditional methods of advertising such as television, newspapers and magazines, its very difficult to attribute sales growth to advertising content, placement, or external factors. AI-driven advertising tools and platforms make this a doddle but are most effective when used as part of a coordinated AI marketing strategy, taking in the other areas of marketing covered here!

Public Relations

Public relations used to focus on the challenge of getting coverage of products and services into mainstream and specialist media publications. In today's online world, the media landscape has exploded, offering opportunities to promote brands directly through social media as well as via influencers and third-party content creators, sponsored and unsponsored. But how do you know where to find the best influencers to bond and cultivate relationships with?

Here once again, AI can help by matching products with people who have cultivated audiences that are likely to be synched to a brands appeal and values. Some uses of AI in this field of marketing involve taking things a step further though, such as AI-generated influencer Lil Miquela who has used chatbot technology to create an entirely digital persona. Despite the fact she doesnt exist, millions of followers consider her an arbiter of style and are happy to go along with her recommendations, meaning she can earn a hefty fee from brands like Calvin Klein and Prada.

Writing press releases, shaping external messaging points, and researching the best outlets (online or digital) for gaining coverage are other PR tasks that can all be augmented by AI.

Content Marketing

Content is king has been accepted wisdom in marketing departments since the dawn of web2.0 and the rise of user-generated content platforms (including social media). But what content is king? And where should we put it? How often, how in-depth or simplified quite simply, how do we make sure our content achieves our aims of establishing our brand, positioning ourselves as experts or authorities in our field, and, of course, eventually generating sales and leads?

Well, one option is to use AI. Buzzfeed is one of the biggest content-driven sites in the world, and Roetzer has examined how it uses AI to drive every aspect of its operations, such as determining the odds of a particular piece of content going viral, suggesting what content visitors would like to see, and automating the routine aspects of publication such as keyword selection, categorization, and personalization. What marks out Buzzfeed as a truly AI-driven content outlet is its strategy-focused approach where every piece of content as well as every user interaction is measured and optimized for insights that can then be put to work anywhere within marketing operations.

Email Marketing

Email marketing is often about tweaking headings, scheduling, and copy in order to impact those all-important open and click-through rates. Small differences in the language that is used can make the difference between an email getting identified as one of the 148 billion spam emails sent each day and snared by a filter or making its way through to the intended recipient at a time when they are open to suggestions on what they should buy.

A large number of AI-powered tools exist to help with these tasks, such as Phrasee, which automates the creation of subject lines; Seventh Sense which optimizes the timing of mailshots; and rasa.io, which makes it easy to create personalized newsletters.

Where next?

Whether AI achieves the potential that clearly exists depends on businesses coming to understand the need for a coordinated and strategic approach to marketing AI implementation. It should be clear enough how the different use cases I have mentioned above can be useful in isolation. But the real value is unlocked when we start using them together, with the aim of answering our most pressing questions, influencing our most important metrics, and achieving key business goals.

Roetzer tells me Its this tricky spot because a lot of business professionals still see AI as some kind of abstract, sci-fi thing I dont think they understand that its extremely approachable, you can test AI today find a tool for $19 per month and try it its not something where you have to spend six months preparing a pilot project.

However, what you do need is people and more specifically, people with the relevant skills. Most marketing departments outside of large enterprise companies wont be appointing dedicated, specialist data scientists and neither should they need to.

As a company goes through the ongoing process of developing a data-and-AI-literate culture, it is more important that it enables people who are already experts in their particular field to upskill and understand the importance of the technology.

When it comes to those who get it totally right "honestly, it's hard to find," Roetzer says.

"Either brands are doing it, and they don't want to talk about it because they think it's a competitive advantage or, they're not actually doing anything maybe just starting to run pilot projects or find someone on their team who can lead this its very hard to find the intersection of business professionals who understand what AI is capable of doing, and can apply it to real business problems and use cases.

You can click here to check out my webinar with Paul Roetzer, CEO and founder of Marketing AI Institute, where we cover many other aspects of AI in marketing, including the questions of machine creativity and AI ethics, as well as take a look at his most recent book, Marketing Artificial Intelligence.

To stay on top of the latest on the latest business and tech trends, make sure to subscribe to my newsletter and follow me on Twitter, LinkedIn, and YouTube.

Read this article:

Artificial Intelligence And The Future Of Marketing - Forbes

DUBLIN--(BUSINESS WIRE)--The "Artificial Intelligence Market Size, Share & Trends Analysis Report by Solution, by Technology (Deep Learning, Machine Learning, Natural Language Processing, Machine Vision), by End Use, by Region, and Segment Forecasts, 2022-2030" report has been added to ResearchAndMarkets.com's offering.

The global artificial intelligence market size is expected to reach USD 1,811.8 billion by 2030. The market is anticipated to expand at a CAGR of 38.1% from 2022 to 2030.

Companies Mentioned

Artificial Intelligence (AI) denotes the concept and development of computing systems capable of performing tasks customarily requiring human assistance, such as decision-making, speech recognition, visual perception, and language translation. AI uses algorithms to understand human speech, visually recognize objects, and process information. These algorithms are used for data processing, calculation, and automated reasoning.

Artificial intelligence researchers continuously improve algorithms for various aspects, as conventional algorithms have drawbacks regarding accuracy and efficiency. These advancements have led manufacturers and technology developers to focus on developing standard algorithms. Recently, several developments have been carried out for enhancing artificial intelligence algorithms. For instance, in May 2020, International Business Machines Corporation announced a wide range of new AI-powered services and capabilities, namely IBM Watson AIOps, for enterprise automation. These services are designed to help automate the IT infrastructures and make them more resilient and cost reduction.

Various companies are implementing AI-based solutions such as RPA (Robotic Process Automation) to enhance the process workflows to handle and automate repetitive tasks. AI-based solutions are also being coupled with the IoT (Internet of Things) to provide robust results for various business processes. For Instance, Microsoft announced to invest USD 1 billion in OpenAI, a San Francisco-based company. The two businesses teamed up to create AI supercomputing technology on Microsoft's Azure cloud.

The COVID-19 pandemic has emerged as an opportunity for AI-enabled computer systems to fight against the epidemic as several tech companies are working on preventing, mitigating, and containing the virus. For instance, LeewayHertz, a U.S.-based custom software development company, offers technology solutions using AI tools and techniques, including the Face Mask Detection System to identify individuals without a mask and the Human Presence System to monitor patients remotely. Besides, Voxel51 Inc., a U.S.-based artificial intelligence start-up, has developed Voxel51 PDI (Physical Distancing Index) to measure the impact of the global pandemic on social behavior across the world.

AI-powered computer platforms or solutions are being used to fight against COVID - 19 in numerous applications, such as early alerts, tracking and prediction, data dashboards, diagnosis and prognosis, treatments and cures, and maintaining social control. Data dashboards that can visualize the pandemic have emerged with the need for coronavirus tracking and prediction. For instance, Microsoft Corporation's Bing's AI tracker gives a global overview of the pandemic's current statistics.

Artificial Intelligence Market Report Highlights

Key Topics Covered:

Chapter 1 Methodology and Scope

Chapter 2 Executive Summary

Chapter 3 Market Variables, Trends & Scope

3.1 Market Trends & Outlook

3.2 Market Segmentation & Scope

3.3 Artificial Intelligence Size and Growth Prospects

3.4 Artificial Intelligence-Value Chain Analysis

3.5 Artificial Intelligence Market Dynamics

3.5.1 Market Drivers

3.5.1.1 Economical parallel processing set-up

3.5.1.2 Potential R&D in artificial intelligence systems

3.5.1.3 Big data fuelling AI and Machine Learning profoundly

3.5.1.4 Increasing Cross-Industry Partnerships and Collaborations

3.5.1.5 AI to counter unmet clinical demand

3.5.2 Market Restraint

3.5.2.1 Vast demonstrative data requirement

3.6 Penetration & Growth Prospect Mapping

3.7 Industry Analysis-Porter's

3.8 Company Market Share Analysis, 2021

3.9 Artificial Intelligence-PEST Analysis

3.10 Artificial Intelligence-COVID-19 Impact Analysis

Chapter 4 Artificial Intelligence Market: Solution Estimates & Trend Analysis

Chapter 5 Artificial Intelligence Market: Technology Estimates & Trend Analysis

Chapter 6 Artificial Intelligence Market: End-Use Estimates & Trend Analysis

Chapter 7 Artificial Intelligence Market: Regional Estimates & Trend Analysis

Chapter 8 Competitive Landscape

For more information about this report visit https://www.researchandmarkets.com/r/ykyt2m

The rest is here:

The Worldwide Artificial Intelligence Industry is Expected to Reach $1811 Billion by 2030 - ResearchAndMarkets.com - Business Wire