Daily Archives: September 11, 2022

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

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Artificial Intelligence But Not Social - Nation World News

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

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

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.

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Artificial Intelligence And The Future Of Marketing - Forbes

Redding, California, Sept. 08, 2022 (GLOBE NEWSWIRE) -- According to a new market research report titled, Artificial Intelligence in Genomics Market by Offering (Software, Services), Functionality (Sequencing, Gene Editing), Application (Diagnostics, Precision Medicine, Drug Discovery and Development), Delivery Mode (On-premise, Cloud) and End User - Global Forecast to 2029.' published by Meticulous Research, AI in genomics market is expected to grow at a CAGR of 42.8% during the forecast period 20222029 to reach $6.22 billion by 2029.

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Artificial intelligence (AI) has tremendous potential in genomics. AI or machine learning (ML) helps to improve understanding of the hidden patterns in complex and large genomics data sets from clinical and research projects. AI in genomics helps identify the genetic disorders and the primary kind of cancer, predict the specific diseases progress in a patient, identify disease-causing genomic variants, and improve the function of gene editing tools.

Impact of COVID-19 on the Artificial Intelligence in Genomics Market

The COVID-19 pandemic has positively impacted the AI in genomics market. The pandemic led to increased investments by pharmaceutical manufacturers to support R&D in COVID-19 vaccine development. In the COVID-19-related research, the integration of AI in genomics plays a significant role in identifying the virus origin, behavior, and structure of SARS-CoV-2, which helps to develop new vaccines or drug therapies. To meet the growing market needs in light of the pandemic, market players developed AI tools to accelerate research on COVID-19. For instance, in May 2020, the NVIDIA Corporation (U.S.) expanded NVIDIA Clara Healthcare Platform offerings to help the medical community better track, treat, and test COVID-19.

The global AI in genomics market study is segmented based on offering (software, services), delivery mode (on-premises, cloud-based/web-based), functionality (genome sequencing, gene editing, other functionalities), application (drug discovery and development, precision medicine, diagnostics, and other applications), end user (pharmaceutical & biopharmaceutical companies, research institutes & academics, and other end users), and geography. The study also evaluates industry competitors and analyzes their market share at global and regional levels.

Speak to our Analysts to Understand the Impact of COVID-19 on Your Business:https://www.meticulousresearch.com/speak-to-analyst/cp_id=5345

Based on offering, in 2022, the software segment is estimated to account for the largest share of the global AI in genomics market. Increased use of AI in genomics software by the pharmaceutical & biotechnology companies for drug discovery and precision medicine is a key factor driving the market. Integrating AI in genomics helps pharmaceutical manufacturers analyze and identify genomic data to reduce the turnaround time for discovery. The rising burden of infectious and non-infectious diseases has increased the responsibility of pharmaceutical manufacturers for new drug development. A large number of molecules in the clinical pipeline are driving the demand for AI in genomics software for drug discovery.

Based on delivery mode, the cloud-based segment is estimated to register the highest CAGR during the forecast period. Advantages offered by cloud-based models, such as high security, affordability, and scalability, are increasing the adoption of cloud-based models in AI in genomics. The adoption of cloud-based models eliminates the need for capital expenditure incurred in creating and maintaining in-house computer clusters. The cloud-based models also offer increased scalability to process large sample data sets.

Based on functionality, in 2022, the genome sequencing segment is estimated to account for the largest share of the global AI in genomics market. The integration of AI in genomics accelerates the speed of genome sequencing to identify genetic patterns. This speeds up the drug discovery process as genome sequencing provides a great understanding of the disease, further ensuring efficacy, safety, and success in drug development. Additionally, the increased demand for genome sequencing during COVID-19 from researchers and medical experts to understand genetic variants has driven the market.

Based on application, in 2022, the drug discovery and development segment is estimated to account for the largest share of the global AI in genomics market. The key factors attributing to the largest share are the growing demand for new and innovative therapies to combat rising chronic or infectious diseases and growing collaborations among the pharmaceutical/ biotechnology companies and market players offering AI in genomics solutions. The integration of AI in genomics accelerates drug discovery, thereby reducing the turnaround time to get the drug on the market.

Quick Buy Artificial Intelligence in Genomics Market - Global Opportunity Analysis And Industry Forecast (2022-2029), Research Report: https://www.meticulousresearch.com/Checkout/54335299

Based on end user, in 2022, the pharmaceutical and biopharmaceutical companies segment is expected to account for the largest share of the global AI in genomics market. Pharmaceutical & biopharmaceutical companies are increasingly relying on AI algorithms to identify disease-causing mutations, design therapeutic targets, and identify appropriate treatment regimens. The increasing pressure on the pharmaceutical companies to reduce the operational cost of drug manufacturing and the subsequent rising prevalence of chronic and infectious diseases are the key factors driving the adoption of AI in the pharmaceutical & biopharmaceutical companies for drug discovery.

Based on geography, in 2022, North America is estimated to account for the largest share of the AI in genomics market, followed by Europe and Asia-Pacific. The U.S. is estimated to account for the largest share of the AI in genomics market in North America in 2022. The market is primarily driven by increasing public and private investment in genomics and the growing adoption of AI solutions in precision medicine to meet the increasing market demand.

The report also includes an extensive assessment of the key strategic developments of leading market participants in the industry over three years (20192022). In the last couple of years, the global AI in genomics market has witnessed collaborations and product launches. For instance:

Some of the key players operating in the global AI in genomics market are Microsoft Corporation (U.S.), NVIDIA Corporation (U.S.), Deep Genomics (Canada), Fabric Genomics, Inc. (U.S.), Data4Cure, Inc. (U.S.), Predictive Oncology Inc. (U.S.), Emedgene (U.S.), Congenica Ltd. (U.K.), Tempus (U.S.), SOPHiA GENETICS (Switzerland).

To gain more insights into the market with a detailed table of content and figures, click here:https://www.meticulousresearch.com/product/ai-in-genomics-market-5345

Scope of the Report:

Artificial Intelligence in Genomics Market, by Offering

Artificial Intelligence in Genomics Market, by Delivery Mode

Artificial Intelligence in Genomics Market, by Functionality

Note: Other functionalities include pharmacogenomics and predictive gene testing.

Artificial Intelligence in Genomics Market, by Application

Note: Other applications include agriculture, pharmacology, and animal health.

Artificial Intelligence in Genomics Market, by End User

Note: Other end users include contract research organizations and agriculture companies.

Artificial Intelligence in Genomics Market, by Geography

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Meticulous Research was founded in 2010 and incorporated as Meticulous Market Research Pvt. Ltd. in 2013 as a private limited company under the Companies Act, 1956. Since its incorporation, the company has become the leading provider of premium market intelligence in North America, Europe, Asia-Pacific, Latin America, and the Middle East & Africa.

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/571/artificial-intelligence-in-genomics-market-2029

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Artificial Intelligence in Genomics Market Worth $6.22 Billion by 2029 - Exclusive Report by Meticulous Research - GlobeNewswire