Monthly Archives: June 2022

To the average person, it must seem as if the field of artificial intelligence is making immense progress. According to the press releases, and some of the more gushing media accounts, OpenAIs DALL-E 2 can seemingly create spectacular images from any text; another OpenAI system called GPT-3 can talk about just about anything; and a system called Gato that was released in May by DeepMind, a division of Alphabet, seemingly worked well on every taskthe company could throw at it. One of DeepMinds high-level executives even went so far as to brag that in the quest for artificial general intelligence (AGI), AI that has the flexibility and resourcefulness of human intelligence, The Game is Over! And Elon Musk said recently that he would be surprised if we didnt have artificial general intelligence by 2029.

Dont be fooled. Machines may someday be as smart as people, and perhaps even smarter, but the game is far from over. There is still an immense amount of work to be done in making machines that truly can comprehend and reason about the world around them. What we really need right now is less posturing and more basic research.

To be sure, there are indeed some ways in which AI truly is making progresssynthetic images look more and more realistic, and speech recognition can often work in noisy environmentsbut we are still light-years away from general purpose, human-level AI that can understand the true meanings of articles and videos, or deal with unexpected obstacles and interruptions. We are still stuck on precisely the same challenges that academic scientists (including myself) having been pointing out for years: getting AI to be reliable and getting it to cope with unusual circumstances.

Take the recently celebrated Gato, an alleged jack of all trades, and how it captioned an image of a pitcher hurling a baseball. The system returned three different answers: A baseball player pitching a ball on top of a baseball field, A man throwing a baseball at a pitcher on a baseball field and A baseball player at bat and a catcher in the dirt during a baseball game. The first response is correct, but the other two answers include hallucinations of other players that arent seen in the image. The system has no idea what is actually in the picture as opposed to what is typical of roughly similar images. Any baseball fan would recognize that this was the pitcher who has just thrown the ball, and not the other way aroundand although we expect that a catcher and a batter are nearby, they obviously do not appear in the image.

A baseball player pitching a ballon top of a baseball field.A man throwing a baseball at apitcher on a baseball field.A baseball player at bat and acatcher in the dirt during abaseball game

Likewise, DALL-E 2 couldnt tell the difference between a red cube on top of a blue cube and a blue cube on top of a red cube. A newer version of the system, released in May, couldnt tell the difference between an astronaut riding a horse and a horse riding an astronaut.

When systems like DALL-E make mistakes, the result is amusing, but other AI errors create serious problems. To take another example, a Tesla on autopilot recently drove directly towards a human worker carrying a stop sign in the middle of the road, only slowing down when the human driver intervened. The system could recognize humans on their own (as they appeared in the training data) and stop signs in their usual locations (again as they appeared in the trained images), but failed to slow down when confronted by the unusual combination of the two, which put the stop sign in a new and unusual position.

Unfortunately, the fact that these systems still fail to be reliable and struggle with novel circumstances is usually buried in the fine print. Gato worked well on all the tasks DeepMind reported, but rarely as well as other contemporary systems. GPT-3 often creates fluent prose but still struggles with basic arithmetic, and it has so little grip on reality it is prone to creating sentences like Some experts believe that the act of eating a sock helps the brain to come out of its altered state as a result of meditation, when no expert ever said any such thing. A cursory look at recent headlines wouldnt tell you about any of these problems.

The subplot here is that the biggest teams of researchers in AI are no longer to be found in the academy, where peer review used to be coin of the realm, but in corporations. And corporations, unlike universities, have no incentive to play fair. Rather than submitting their splashy new papers to academic scrutiny, they have taken to publication by press release, seducing journalists and sidestepping the peer review process. We know only what the companies want us to know.

In the software industry, theres a word for this kind of strategy: demoware, software designed to look good for a demo, but not necessarily good enough for the real world. Often, demoware becomes vaporware, announced for shock and awe in order to discourage competitors, but never released at all.

Chickens do tend to come home to roost though, eventually. Cold fusion may have sounded great, but you still cant get it at the mall. The cost in AI is likely to be a winter of deflated expectations. Too many products, like driverless cars, automated radiologists and all-purpose digital agents, have been demoed, publicizedand never delivered. For now, the investment dollars keep coming in on promise (who wouldnt like a self-driving car?), but if the core problems of reliability and coping with outliers are not resolved, investment will dry up. We will be left with powerful deepfakes, enormous networks that emit immense amounts of carbon, and solid advances in machine translation, speech recognition and object recognition, but too little else to show for all the premature hype.

Deep learning has advanced the ability of machines to recognize patterns in data, but it has three major flaws. The patterns that it learns are, ironically, superficial, not conceptual; the results it creates are difficult to interpret; and the results are difficult to use in the context of other processes, such as memory and reasoning. As Harvard computer scientist Les Valiant noted, The central challenge [going forward] is to unify the formulation of learning and reasoning. You cant deal with a person carrying a stop sign if you dont really understand what a stop sign even is.

For now, we are trapped in a local minimum in which companies pursue benchmarks, rather than foundational ideas, eking out small improvements with the technologies they already have rather than pausing to ask more fundamental questions. Instead of pursuing flashy straight-to-the-media demos, we need more people asking basic questions about how to build systems that can learn and reason at the same time. Instead, current engineering practice is far ahead of scientific skills, working harder to use tools that arent fully understood than to develop new tools and a clearer theoretical ground. This is why basic research remains crucial.

That a large part of the AI research community (like those that shout Game Over) doesnt even see that is, well, heartbreaking.

Imagine if some extraterrestrial studied all human interaction only by looking down at shadows on the ground, noticing, to its credit, that some shadows are bigger than others, and that all shadows disappear at night, and maybe even noticing that the shadows regularly grew and shrank at certain periodic intervalswithout ever looking up to see the sun or recognizing the three-dimensional world above.

Its time for artificial intelligence researchers to look up. We cant solve AI with PR alone.

This is an opinion and analysis article, and the views expressed by the author or authors are not necessarily those of Scientific American.

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Artificial General Intelligence Is Not as Imminent as You Might Think - Scientific American

DUBLIN--(BUSINESS WIRE)--The "Global Artificial Intelligence (AI) Robots Market (2022-2027) by Offering, Robot, Technology, Deployment Mode, Application, Geography, Competitive Analysis, and the Impact of Covid-19 with Ansoff Analysis" report has been added to ResearchAndMarkets.com's offering.

The Global Artificial Intelligence (AI) Robots Market is estimated to be USD 7.1 Bn in 2022 and is projected to reach USD 38.32 Bn by 2027, growing at a CAGR of 40.1%.

Market dynamics are forces that impact the prices and behaviors of the Global Artificial Intelligence (AI) Robots Market stakeholders. These forces create pricing signals which result from the changes in the supply and demand curves for a given product or service. Forces of Market Dynamics may be related to macro-economic and micro-economic factors. There are dynamic market forces other than price, demand, and supply. Human emotions can also drive decisions, influence the market, and create price signals.

As the market dynamics impact the supply and demand curves, decision-makers aim to determine the best way to use various financial tools to stem various strategies for speeding the growth and reducing the risks.

Competitive Quadrant

The report includes Competitive Quadrant, a proprietary tool to analyze and evaluate the position of companies based on their Industry Position score and Market Performance score. The tool uses various factors for categorizing the players into four categories. Some of these factors considered for analysis are financial performance over the last 3 years, growth strategies, innovation score, new product launches, investments, growth in market share, etc.

Ansoff Analysis

The report presents a detailed Ansoff matrix analysis for the Global Artificial Intelligence (AI) Robots Market. Ansoff Matrix, also known as Product/Market Expansion Grid, is a strategic tool used to design strategies for the growth of the company. The matrix can be used to evaluate approaches in four strategies viz. Market Development, Market Penetration, Product Development and Diversification. The matrix is also used for risk analysis to understand the risk involved with each approach.

The report analyses the Global Artificial Intelligence (AI) Robots Market using the Ansoff Matrix to provide the best approaches a company can take to improve its market position.

Based on the SWOT analysis conducted on the industry and industry players, the analyst has devised suitable strategies for market growth.

Why buy this report?

Market Dynamics

Drivers

Restraints

Opportunities

Challenges

Market Segmentations

The Global Artificial Intelligence (AI) Robots Market is segmented based on Offering, Robot, Technology, Deployment Mode, Application, and Geography.

Companies Mentioned

For more information about this report visit https://www.researchandmarkets.com/r/510pev

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Outlook on the Artificial Intelligence Robots Global Market to 2027 - Focus on Developing Robots with Special Application Cases Presents Opportunities...

Redding, California, June 09, 2022 (GLOBE NEWSWIRE) -- According to a new market research report titled, AI in Cybersecurity Market by Technology (ML, NLP), Security (Endpoint, Cloud, Network), Application (DLP, UTM, IAM, Antivirus, IDP), Industry (Retail, Government, BFSI, IT, Healthcare), and Geography - Global Forecasts to 2029, the global artificial intelligence in cybersecurity market is expected to grow at a CAGR of 24.2% during the forecast period to reach $66.22 billion by 2029.

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The increasing demand for advanced cybersecurity solutions and privacy, the growing significance of AI-based cybersecurity solutions in the banking sector, the rising frequency and complexity of cyber threats are the key factors driving the growth of the artificial intelligence in cybersecurity market. In addition, the growing need for AI-based cybersecurity solutions among small and medium-sized enterprises (SMEs) are creating new growth opportunities for vendors in the AI in cybersecurity market.

However, the lack of skilled AI professionals, the perception of AI in cybersecurity as an uncomprehensive security solution, and the impacts of the COVID-19 pandemic are expected to restrain the growth of this market to a notable extent.

The global artificial intelligence in cybersecurity market is segmented based on components (hardware, software, services), technology (machine learning, natural language processing, context-aware computing), security (application security, endpoint security, cloud security, network security), by applications (data loss prevention, unified threat management, encryption, identity & access management, risk & compliance management, antivirus/antimalware, intrusion detection/prevention system, distributed denial of service mitigation, security information & event management, threat intelligence, fraud detection), by deployment (on-premises, cloud-based), industry vertical (retail, government & defense, automotive & transportation, BFSI, manufacturing, infrastructure, IT & telecommunication, healthcare, aerospace, education, energy). The study also evaluates industry competitors and analyses the market at the country level.

Based on component, the AI in cybersecurity market is segmented into software, hardware, and services. In 2022, the software segment is estimated to account for the largest share of the artificial intelligence in cybersecurity market. The larger share and highest CAGR of this segment is primarily driven by the growing data security concerns, the increase in demand for AI platforms solutions for security operations, the surge in demand for robust and cost-effective security solutions among business enterprises to strengthen their cybersecurity infrastructure.

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Based on technology, the market is segmented into machine learning, natural language processing (NLP), and context-aware computing. In 2022, the machine learning technology segment is estimated to account for the largest share of the artificial intelligence in cybersecurity market. The large share and highest CAGR of this segment is primarily attributed to its advanced ability to collect, process, and handle big data from different sources that offer rapid analysis and prediction. It also helps analyze user behavior and learn from them to help prevent attacks and respond to changing behavior. In addition, it helps find threats and respond to active attacks in real-time, reduces the amount of time spent on routine tasks, and enables organizations to use their resources more strategically, further supporting the growth of the machine learning technology market in the coming years.

Based on security, the market is segmented into network security, cloud security, endpoint security, and application security. In 2022, the network security segment is estimated to account for the largest share of the artificial intelligence in cybersecurity market. The large share of this segment is attributed to the adoption of the Bring Your Own Device (BYOD) trend, the increasing number of APTs, malware, and phishing attacks, the increasing need for secure data transmission, the growing demand for network security solutions, and rising privacy concerns. However, the cloud security segment is slated to register the highest CAGR during the forecast period due to the increased adoption of Internet of Things (IoT) devices, surge in the deployment of cloud solutions, the emergence of remote work and collaboration, the increasing demand for robust and cost-effective security services.

Based on application, this market is segmented into data loss prevention, unified threat management, encryption, identity & access management, risk & compliance management, intrusion detection/prevention system, antivirus/antimalware, distributed denial of service (DDoS) mitigation, Security Information and event management (SIEM), threat intelligence, and fraud detection. In 2022, the identity and access management segment is estimated to account for the largest share of the artificial intelligence in cybersecurity market. The large share of this segment is attributed to the increase in security concerns among organizations, the increasing number and complexity of cyber-attacks, the growing need for integrity & safety of confidential information in industry verticals, and the growing emphasis on compliance management. However, the data loss prevention segment is slated to register the highest CAGR during the forecast period due to the increasing regulatory and compliance requirements and the growing need to address data-related threats, including the risks of accidental data loss and exposure of sensitive data in organizations.

Quick Buy Artificial Intelligence in Cybersecurity Market by Technology (ML, NLP), Security (Endpoint, Cloud, Network), Application (DLP, UTM, IAM, Antivirus, IDP), Industry (Retail, Government, BFSI, IT, Healthcare), and Region - Global Forecasts to 2029 Research Report: https://www.meticulousresearch.com/Checkout/30331808

Based on industry vertical, the market is segmented into government & defense, retail, manufacturing, banking, financial services, and insurance (BFSI), automotive & transportation, healthcare, IT & telecommunication, aerospace, education, and energy. In 2022, the IT & telecommunication sector is estimated to account for the largest share of the AI in cybersecurity market. The large share of this segment is mainly attributed to increasing incidence of security breaches by cybercriminal, shifting preference from traditional business models to sophisticated technologies, and including IoT devices, 5G, and cloud computing. However, the healthcare sector is slated to register the highest CAGR during the forecast period due to the rising sophistication levels of cyber-attacks, the growing incorporation of advanced cybersecurity solutions, the exponential rise in healthcare data breaches, and the growing adoption of IoT & connected devices across the healthcare sector.

Based on deployment, the market is segmented into on-premises and cloud-based. In 2022, the on-premises segment is estimated to account for the largest share of the artificial intelligence in cybersecurity market. The large share of this segment is attributed to the increasing necessity for enhancing the internal processes & systems, security issues related to cloud-based deployments, and the rising demand for advanced security application software among industry verticals. However, the cloud-based segment is slated to register the highest CAGR during the forecast period due to the increasing number of large enterprises using cloud platforms for data repositories and the growing demand to reduce the capital investment required to implement cybersecurity solutions. In addition, several organizations are moving operations to the cloud, leading cybersecurity vendors to develop cloud-based solutions.

Based on geography, in 2022, North America is estimated to account for the largest share of the overall artificial intelligence in cybersecurity market. The large market share of North America is attributed to the presence of major players along with several emerging startups in the region, the increase in government initiatives towards advanced technologies, such as artificial intelligence, the proliferation of cloud-based solutions, the increasing sophistication in cyber-attacks, and the emergence of disruptive digital technologies. However, Asia-Pacific is expected to register the highest CAGR during the forecast period. Factors such as the rising number of connected devices, the increasing privacy & security concerns, the growing awareness regarding cybersecurity among organizations, rapid economic development, high adoption of advanced technologies, such as IoT, 5G technology, and cloud computing are contributing to the growth of this market in Asia-Pacific.

The report also includes an extensive assessment of the key strategic developments adopted by the leading market participants in the industry over the past four years (20192022). The artificial intelligence in cybersecurity market has witnessed several partnerships & agreements in recent years that enabled companies to broaden their product portfolios, advance the capabilities of existing products, and gain cost leadership in the cybersecurity market. For instance, in 2021, Juniper Networks, Inc. (U.S.) launched Juniper Cloud Workload Protection, a software designed to automatically defend application workloads in any cloud or on-premises data center environment against application exploits in real-time. Similarly, in 2021, SecurityBridge (Germany) partnered with Fortinet, Inc. (U.S.) to address the security challenges posed by vulnerabilities within the SAP landscape. Also, in 2021, Check Point Software Technologies Ltd. (Israel) launched security gateways to protect SMBs against threats.

The global artificial intelligence in cybersecurity market is fragmented in nature. The major players operating in this market are Amazon Web Services, Inc. (U.S.), IBM Corporation (U.S.), Intel Corporation (U.S.), Microsoft Corporation (U.S.), Nvidia Corporation (U.S.), FireEye, Inc. (U.S.), Palo Alto Networks, Inc. (U.S.), Juniper Networks, Inc. (U.S.), Fortinet, Inc. (U.S.), Cisco Systems, Inc. (U.S.), Micron Technology, Inc. (U.S.), Check Point Software Technologies Ltd. (U.S.), Imperva (U.S.), McAfee LLC (U.S.), LogRhythm, Inc. (U.S.), Sophos Ltd. (U.S.), NortonLifeLock Inc. (U.S.), and Crowdstrike Holdings, Inc. (U.S.).

To gain more insights into the market with a detailed table of content and figures, click here:https://www.meticulousresearch.com/product/artificial-intelligence-in-cybersecurity-market-5101

Scope of the Report:

AI in CybersecurityMarket by Component

AI in CybersecurityMarket by Technology

AI in CybersecurityMarket by Security Type

AI in CybersecurityMarket by Application

AI in Cybersecurity Market by Deployment Type

AI in CybersecurityMarket by Industry Vertical

AI in CybersecurityMarket by Geography:

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The name of our company defines our services, strengths, and values. Since the inception, we have only thrived to research, analyze, and present the critical market data with great attention to details. With the meticulous primary and secondary research techniques, we have built strong capabilities in data collection, interpretation, and analysis of data including qualitative and quantitative research with the finest team of analysts. We design our meticulously analyzed intelligent and value-driven syndicate market research reports, custom studies, quick turnaround research, and consulting solutions to address business challenges of sustainable growth.

Contact:Mr.Khushal BombeMeticulous Market Research Inc.1267WillisSt,Ste200 Redding,California,96001, U.S.USA: +1-646-781-8004Europe : +44-203-868-8738APAC: +91 744-7780008Email-sales@meticulousresearch.comVisit Our Website:https://www.meticulousresearch.com/Connect with us on LinkedIn-https://www.linkedin.com/company/meticulous-researchContent Source: https://www.meticulousresearch.com/pressrelease/56/artificial-intelligence-in-cybersecurity-market-2029

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Artificial Intelligence in Cybersecurity Market Worth $66.22 Billion by 2029 - Exclusive Report by Meticulous Research - GlobeNewswire

Dublin, June 08, 2022 (GLOBE NEWSWIRE) -- The "Global Artificial Intelligence (AI) Partnering Terms and Agreements 2010 to 2022" report has been added to ResearchAndMarkets.com's offering.

This report contains a comprehensive listing of all artificial intelligence partnering deals announced since 2010 including financial terms where available including over 750 links to online deal records of actual artificial intelligence partnering deals as disclosed by the deal parties.

The report provides a detailed understanding and analysis of how and why companies enter artificial intelligence partnering deals. The majority of deals are early development stage whereby the licensee obtains a right or an option right to license the licensors artificial intelligence technology or product candidates. These deals tend to be multicomponent, starting with collaborative R&D, and commercialization of outcomes.

This report provides details of the latest artificial intelligence, oligonucletides including aptamers agreements announced in the healthcare sectors.

Understanding the flexibility of a prospective partner's negotiated deals terms provides critical insight into the negotiation process in terms of what you can expect to achieve during the negotiation of terms. Whilst many smaller companies will be seeking details of the payments clauses, the devil is in the detail in terms of how payments are triggered - contract documents provide this insight where press releases and databases do not.

In addition, where available, records include contract documents as submitted to the Securities Exchange Commission by companies and their partners.

Contract documents provide the answers to numerous questions about a prospective partner's flexibility on a wide range of important issues, many of which will have a significant impact on each party's ability to derive value from the deal.

In addition, a comprehensive appendix is provided organized by artificial intelligence partnering company A-Z, deal type definitions and artificial intelligence partnering agreements example. Each deal title links via Weblink to an online version of the deal record and where available, the contract document, providing easy access to each contract document on demand.

The report also includes numerous tables and figures that illustrate the trends and activities in artificial intelligence partnering and dealmaking since 2010.

In conclusion, this report provides everything a prospective dealmaker needs to know about partnering in the research, development and commercialization of artificial intelligence technologies and products.

Report scope

Global Artificial Intelligence Partnering Terms and Agreements includes:

In Global Artificial Intelligence Partnering Terms and Agreements, the available contracts are listed by:

Key Topics Covered:

Executive Summary

Chapter 1 - Introduction

Chapter 2 - Trends in artificial intelligence dealmaking2.1. Introduction2.2. Artificial intelligence partnering over the years2.3. Most active artificial intelligence dealmakers2.4. Artificial intelligence partnering by deal type2.5. Artificial intelligence partnering by therapy area2.6. Deal terms for artificial intelligence partnering2.6.1 Artificial intelligence partnering headline values2.6.2 Artificial intelligence deal upfront payments2.6.3 Artificial intelligence deal milestone payments2.6.4 Artificial intelligence royalty rates

Chapter 3 - Leading artificial intelligence deals3.1. Introduction3.2. Top artificial intelligence deals by value

Chapter 4 - Most active artificial intelligence dealmakers4.1. Introduction4.2. Most active artificial intelligence dealmakers4.3. Most active artificial intelligence partnering company profiles

Chapter 5 - Artificial intelligence contracts dealmaking directory5.1. Introduction5.2. Artificial intelligence contracts dealmaking directory

Chapter 6 - Artificial intelligence dealmaking by technology type

AppendicesAppendix 1 - Artificial intelligence deals by company A-ZAppendix 2 - Artificial intelligence deals by stage of developmentAppendix 3 - Artificial intelligence deals by deal typeAppendix 4 - Artificial intelligence deals by therapy areaAppendix 5 - Deal type definitionsAppendix 6 - Further reading on dealmaking

Table of figuresFigure 1: Artificial intelligence partnering since 2010Figure 2: Active artificial intelligence dealmaking activity since 2010Figure 3: Artificial intelligence partnering by deal type since 2010Figure 4: Artificial intelligence partnering by disease type since 2010Figure 5: Artificial intelligence deals with a headline valueFigure 6: Artificial intelligence deals with an upfront valueFigure 7: Artificial intelligence deals with a milestone valueFigure 8: Artificial intelligence deals with a royalty rate valueFigure 9: Top artificial intelligence deals by value since 2010Figure 10: Most active artificial intelligence dealmakers since 2010

Companies Mentioned

For more information about this report visit https://www.researchandmarkets.com/r/irt217

About ResearchAndMarkets.comResearchAndMarkets.com is the world's leading source for international market research reports and market data. We provide you with the latest data on international and regional markets, key industries, the top companies, new products and the latest trends.

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Global Artificial Intelligence (AI) Partnering Deal Terms and Agreements Report 2022: Latest AI, Oligonucletides Including Aptamers Agreements...

Netradyne Uses AI To Help Fleets Reduce Driver Incidents, Protect Against False Claims, and Create Safer Roads

SAN DIEGO, June 8, 2022 /PRNewswire/ --Netradyne, an industry leader in artificial intelligence (AI) and edge computing focusing on driver and fleet safety, has been named on this year's Forbes AI 50 list 2022 for North America. Produced in partnership with Sequoia Capital, this list recognizes the standout privately held companies in North America that are making the most interesting and impactful uses of AI.

Forbes editorial team acknowledged that AI technology is driving advancements in every industry but that it can be difficult to identify which companies are utilizing such technology in transformative and measurable ways. The Forbes AI 50 list, now in its fourth edition, identifies North America's privately held companies at the forefront of the field for whom AI is at the heart of their products and services.

In selecting honorees for this year's list, Forbes' 12-judge panel of experts in artificial intelligence from the fields of academia, technology, and venture capital evaluated hundreds of submissions, handpicking the top 50 most compelling companies.

"We are honored to be named to the Forbes AI 50 list," said Avneesh Agrawal, co-founder, and CEO of Netradyne. "At Netradyne, our mission is to create safer and smarter roadways for all. Using AI and edge computing technologies, we are revolutionizing the fleet transportation ecosystem by helping reinforce good driving behavior and similarly empowering drivers to improve their performance."

Agrawal continued, "Driveri's unique ability to analyze everymile of a journey allows insights into good driving behaviors, which can be recognized and rewarded to reinforce drivers' safe behavior, and drivers also have full transparency and coaching access to their personalized driving GreenZone score via the driver mobile app."

Netradyne provides fleets of all sizes and vehicle types with an advanced video safety camera, fleet performance analytics tracking, and driver awareness tools to help reduce risky driving behavior and reward safe driving decision-making. Driveriis the only solution that can positively recognize, empower and improve driver performance. The cascading effects are powerful by using Driveri's revolutionary AI and reinforcing good behavior to improve driver performance in real-time. Fleets see reduced accidents, higher safety scores, lower insurance costs, improved driver retention, and better fleet performance in increased profits.

Netradyne was one of the hundreds of applicants to be included in this prestigious list. A panel of 12 expert AI judges identified the 50 most compelling companies.

About Netradyne, Inc.

Netradyne harnesses the power of Computer Vision and Edge Computing to revolutionize the modern-day transportation ecosystem. Netradyne is an industry leader in fleet safety solutions, immediately improving driver behavior and fleet performance and setting commercial vehicle driving standards. Netradyne collects and analyzes more data points and meaningful information than any other fleet safety organization so customers can improve retention, increase profitability, enhance safety, and enable end-to-end transparency. Organizations trust Netradyne to build a positive, safe, and driver-focused culture to take their business to the next level.

CONTACT: [emailprotected]

SOURCE Netradyne

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Netradyne Named to Forbes AI 50 List of Top Artificial Intelligence Companies of 2022 - PR Newswire

By Vic Medina| 19 hours ago

According to a new report, robots using advanced artificial intelligence are now capable of producing cloned pigs. and thats something that we should totally never worry about, yall. Few could have predicted that in 2022, technology would have developed so quickly, that we could rely on A.I. and robots to create life. While the motivations behind this advancement were noble, it opens up a terrifying scenario only previously seen in science fiction, in which humans no longer have the only say in the course of life on Earth. On the plus side, humanity is about to get more bacon than it could ever dream of, which many might consider a worthy trade-off.

This scientific insanity, of course, comes from China, which apparently has never seen the Terminator films, and which has not had the best track record recently in predicting the consequences of scientific research, particularly, with new viruses. The South China Morning Post reports that robots can now carry out a fully-automated cloning process, thanks to researchers at the University of Nankais College of Artificial Intelligence, where we will most likely see the creation of Skynet and the downfall of mankind. Scientists there claim they have facilitated the birth of seven piglets via surrogate sow. This was achieved through the use of robots programmed with artificial intelligence and without any human involvement whatsoever.

The motivation here is not short-sighted or malicious: its because China is facing a serious food shortage. With a booming populace comes the need for more food (particularly meat) that China has had trouble producing in recent years. Chinese people love pork in fact, its the countrys most popular meat (hey, theyre just like us!). A recent swine virus outbreak nearly crippled the countrys pork-producing industry, so researchers set out to find a way to clone pigs to provide meat more quickly than natural processes. Thats where artificial intelligence steps in, and according to researchers, the A.I. has actually made the cloning process faster and less error-prone the manual human attempts.

That means China could eventually begin large-scale commercial pig cloning as a food source, solving lots of problems for their people. Of course, there is always a concern that a clone pig army could rise up to destroy mankind. If you think thats far-fetched, consider what regular pigs in the Bahamas do when you disturb their day at the beach.

To clone a pig or anything else scientists must physically implant the DNA from a donors somatic (body) cell into an egg cell, called an enucleated oocyte. The resulting fertilization will create an embryo that would eventually grow into a life form identical to the donor, in this case, another tasty pig. The problem with cloning, as it stands today, is that the implanting of DNA must be done by hand, and it often causes damage that destroys the cells. By using robots and artificial intelligence to streamline the implanting process, the transfer is quicker and less likely to produce damage. It also eliminates the need for humans to be involved in the process. Indeed, one of the researchers named Liu Yaowei told a reporter Our AI-powered system can calculate the strain within a cell and direct the robot to use minimal force to complete the cloning process, which reduces the cell damage caused by human hands.

More viable pig embryos mean more pigs to eat, from genetic stock that is less prone to disease and defects than farm-raised pigs. That would allow China to feed its people without having to import those dirty capitalist pigs from America, which it has had to do in recent years after the virus outbreak. With robots and artificial intelligence making cloning on a large scale an actual possibility, however, ethical questions are raised, it remains to be seen if the practice could extend past pigs to other life forms, including humans. But until then, enjoy your BLTs.

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Artificial Intelligence Is Now Producing Cloned Pigs - Giant Freakin Robot

You've probably heard for years that the workforce would be supplanted by robots. AI has changed several roles, such as using self-checkouts, ATMs, and customer support chatbots. The goal is not to scare people, but to highlight the fact that AI is constantly altering lives and executing activities to replace the human workforce. At the same time, technological advancements are producing new career prospects. AI is predicted to increase the demand for professionals, particularly in robotics and software engineering. As a result, AI has the potential to eliminate millions of current occupations while also creating millions of new ones.

Among the many concerns that AI raises is the possibility of wiping out a large portion of the human workforce by eliminating the need for manual labour. But it will simultaneously liberate humans from having to perform tedious, repetitive tasks, allowing them to focus on more complex and rewarding projects, or simply take some much-needed time off.

These figures can make people feel uneasy and anxious about the future. However, history suggests that this may not be the case; there is no question that some industries will be transformed to the point where they no longer require human labour, leading toward job redefinition and business process reform. For eg, the diagnosis of many health issues could be effectively automated, making doctors focus on other major issues that need their attention. In terms of replacing humans completely, human labour is and will continue to be necessary for the foreseeable future.

Until now, we are talking about the jobs that can be snatched as technology advances but then, the human aspects of work cannot be replaced. Let's focus on something that they cannot do. There are some jobs that only humans are capable of performing.

There are jobs that require creation, conceptualization, complex strategic planning, and dealing with unknown spaces and feelings or emotional interactions that are way beyond the expertise of an AI as of now. Let's now talk about certain skills that are irreplaceable till the human race exist.1. Empathy is unique to humans: Some may argue that animals show empathy as well, but they are not the ones taking over the jobs. Humans, unlike programmed software designed to produce a specific result, are capable of feeling emotions. It may seem contradictory, but the personal affinity between a person and an organisation is the foundation of a professional relationship. Humans need a personal connection that extends beyond the professional realm to develop trust and human connection, something that bot technology completely lacks.2. Emotional Intelligence: Though accurate, the AI is not intuitive, or culturally sensitive because that's a human trait. No matter how accurately it is programmed to carry out a task, it cannot possess the human ability to adjust to the algorithm of human intellect. For instance, reading into the situation or the face of another human. It lacks emotional intellect which makes humans capable of understanding and handling an interaction that needs emotional communication. Exactly during your customer care service, one would always prefer a human interaction to read and understand the situation than an automated machine that cannot work or help beyond the programming.

3. Creativity: Perk of being human: AI can improve productivity and efficiency by reducing errors and repetition and replacing manual jobs with intelligent automated solutions, but it cannot comprehend human psychology. Furthermore, as the world becomes more AI-enabled, humans will be able to take on increasingly innovative tasks.

4. Problem-solving outside a code: Humans can deal with unexpected uncertainty by analysing the situation; like critical thinking during complex scenarios, and adopting

There is not even the slightest doubt that AI will not drive the future. To make AI work, humans need to be creative, insightful, and contextually aware. The reason for this is straightforward: humans will continue to provide value that machines cannot duplicate.

(Amarvijayy Taandur, CBO - BYLD group for Crucial Learning)

Link:

Four skills that won't be replaced by Artificial Intelligence in the future - Economic Times