What does Integration of Artificial Intelligence and Advanced Analytics mean in Business?

Disruptive technologies like artificial intelligence (AI) and advanced analytics have had a transformational impact on the finance industry. They are also changing the way enterprises interact with their clients and run their organizations. The emergence and rapid growth of these technologies helped companies enhance their processes and operations.

While data analytics refers to drawing insights from raw data, advanced analytics help collate previously untapped data sources, especially the unstructured data and data from the intelligent edge, to garner analytical insights. Meanwhile, artificial intelligence replicates behaviors that are generally associated with human intelligence. These include learning, reasoning, problem-solving, planning, perception, and manipulation. Some latest iterations of AI, like generative AI, can also create creative artwork, music, and more. Though these technologies sound diverse, their synergy would bring tremendous innovation across several industries. When powered by AI, advanced analytics algorithms can offer additional performance over other analytics techniques.

World Economic Forum states that the COVID-19 crisis provided a chance for advanced analytics and AI-based techniques to augment decision-making among business leaders too.

In a study conducted by Forrester Consulting on behalf of Intel, 98% of respondents believe that analytics is crucial to driving business priorities. Yet, fewer than 40% of workloads are leveraging advanced analytics or artificial intelligence. For instance, according to Deloitte Insights, only 70% of all financial services firms use machine learning to predict cash flow events, fine-tune credit scores, and detect fraud.

Advanced analytics and artificial intelligence are emerging favorites in the finance sector as they help firms authenticate customers, improve customer experience, and reduce the cost of maintaining acceptable levels of fraud risk, particularly in digital channels.As finance firms race inch to disruption, the velocity of fraud attacks and threats also increases. The amalgamation of these technologies helps mitigate such threats before there is any severe damage, thus increasing compliance. This is achieved by assessing risks, identifying potential suspicious activities, preventing fraudulent transactions, and more. Since AI powered analytical algorithms are adept at pattern recognition and processing large quantities of data, it is key to improving fraud detection rates. For customers, they can help authenticate any financial services they may be using and issue alert the customer if something is wrong.

This fraud detection capability is also helpful for brand marketers to distinguish successful campaigns and avoid wasteful spending. Boston Consulting Group has observed that consumer packaged goods (CPG) companies can boost more than 10% of their revenue growth through enhanced predictive demand forecasting, relevant local assortments, personalized consumer services, and experiences, optimized marketing and promotion ROI, and faster innovation cycles; all via the said technologies.

While factors like data silos, fear of missing out on the race to digital transformation and agility have influenced companies to rely on data-driven insights, they must leverage advanced analytics and artificial intelligence, to stay relevant in the market. In its September 2017 article, titledHow Big Consumer Companies Can Fight Back, Boston Consulting Group also mentions that these technologies top industry players can use them to transform their data into valuable insights. In other words, it can augment an enterprises ability to execute data-intensive workloads and, at the same time, keep the HPC environment adaptable, responsive, and cost-effective.

However, there are many difficulties faced by companies when adopting them too. As per a research survey by Ericsson IndustryLab, 91% of organizations surveyed reported facing problems in each of three categories of challenges studied, including technology, organizational, and company culture and people. It is true that artificial intelligence and advanced analytics tools allowed navigation and the re-imagining of all aspects of business operations, and the COVID-19 pandemic expedited their adoption. However, despite beingarguably the most powerful general-purpose technologies, companies must recognize potential, use cases, and strategize the right action plans to accelerate their artificial intelligence and advanced analytics undertakings.

Share This ArticleDo the sharing thingy

See the article here:
What does Integration of Artificial Intelligence and Advanced Analytics mean in Business? - Analytics Insight

WASHINGTON The Pentagons top artificial intelligence office, called the Joint Artificial Intelligence Center, is shifting its focus and wants 2021 to be the year that it becomes the central repository for military components looking to use AI.

How will we know when JAIC is successful? Its when the term JAIC is used in conversations at all levels, DoD CIO Dana Deasy said in late November on a FedScoop webinar. [Its] when people will say, Was that data run through JAIC? Did those algorithms get pushed out today through JAIC? Did you guys go to JAIC and put that in the library? Did you go and look at the integration solutions from JAIC?

The term that word JAIC starts to get used in the vernacular of peoples day-to-day conversations, then that all feels like the original vision that we put in place for Jake is really starting to be brought to life.

The cornerstone to that effort, according to Deasy and JAIC Director Lt. Gen. Michael Groen, is the Joint Common Foundation, a central repository being built by the JAIC through a $106 million award to Deloitte. Services can use that platform to get tools, models and other software to develop artificial intelligence programs.

The goal is for JCF to be a place where personnel can bring their data, and the JAIC can provide services such as labeling, curation and eventually, algorithm storage and cataloging, Groen said.

One of the things weve discovered is the problems across the department that we can solve through AI, they cluster meaning you can ... reuse algorithms across different applications, he said.

The JAIC also will provide soft services, such as assistance with test and evaluation and contracts.

A lot of cases, especially some of our more advanced partners, all they need is like access to a contract vehicle. They dont really need anything else, so we can do that for them, Groen said.

Sign up for the C4ISRNET newsletter about future battlefield technologies.

(please select a country) United States United Kingdom Afghanistan Albania Algeria American Samoa Andorra Angola Anguilla Antarctica Antigua and Barbuda Argentina Armenia Aruba Australia Austria Azerbaijan Bahamas Bahrain Bangladesh Barbados Belarus Belgium Belize Benin Bermuda Bhutan Bolivia Bosnia and Herzegovina Botswana Bouvet Island Brazil British Indian Ocean Territory Brunei Darussalam Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Cape Verde Cayman Islands Central African Republic Chad Chile China Christmas Island Cocos (Keeling) Islands Colombia Comoros Congo Congo, The Democratic Republic of The Cook Islands Costa Rica Cote D'ivoire Croatia Cuba Cyprus Czech Republic Denmark Djibouti Dominica Dominican Republic Ecuador Egypt El Salvador Equatorial Guinea Eritrea Estonia Ethiopia Falkland Islands (Malvinas) Faroe Islands Fiji Finland France French Guiana French Polynesia French Southern Territories Gabon Gambia Georgia Germany Ghana Gibraltar Greece Greenland Grenada Guadeloupe Guam Guatemala Guinea Guinea-bissau Guyana Haiti Heard Island and Mcdonald Islands Holy See (Vatican City State) Honduras Hong Kong Hungary Iceland India Indonesia Iran, Islamic Republic of Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Kiribati Korea, Democratic People's Republic of Korea, Republic of Kuwait Kyrgyzstan Lao People's Democratic Republic Latvia Lebanon Lesotho Liberia Libyan Arab Jamahiriya Liechtenstein Lithuania Luxembourg Macao Macedonia, The Former Yugoslav Republic of Madagascar Malawi Malaysia Maldives Mali Malta Marshall Islands Martinique Mauritania Mauritius Mayotte Mexico Micronesia, Federated States of Moldova, Republic of Monaco Mongolia Montserrat Morocco Mozambique Myanmar Namibia Nauru Nepal Netherlands Netherlands Antilles New Caledonia New Zealand Nicaragua Niger Nigeria Niue Norfolk Island Northern Mariana Islands Norway Oman Pakistan Palau Palestinian Territory, Occupied Panama Papua New Guinea Paraguay Peru Philippines Pitcairn Poland Portugal Puerto Rico Qatar Reunion Romania Russian Federation Rwanda Saint Helena Saint Kitts and Nevis Saint Lucia Saint Pierre and Miquelon Saint Vincent and The Grenadines Samoa San Marino Sao Tome and Principe Saudi Arabia Senegal Serbia and Montenegro Seychelles Sierra Leone Singapore Slovakia Slovenia Solomon Islands Somalia South Africa South Georgia and The South Sandwich Islands Spain Sri Lanka Sudan Suriname Svalbard and Jan Mayen Swaziland Sweden Switzerland Syrian Arab Republic Taiwan, Province of China Tajikistan Tanzania, United Republic of Thailand Timor-leste Togo Tokelau Tonga Trinidad and Tobago Tunisia Turkey Turkmenistan Turks and Caicos Islands Tuvalu Uganda Ukraine United Arab Emirates United Kingdom United States United States Minor Outlying Islands Uruguay Uzbekistan Vanuatu Venezuela Viet Nam Virgin Islands, British Virgin Islands, U.S. Wallis and Futuna Western Sahara Yemen Zambia Zimbabwe

Subscribe

By giving us your email, you are opting in to the C4ISRNET Daily Brief.

This year, the JAIC expanded its mission focuses to include joint war fighting, an important mission given the military services focuses on multidomain operations a concept that will require artificial intelligence to increase the speed at which data flows and commanders make decisions. In calendar year 2021, the JAIC will focus on war fighter integration and creation of an artificial intelligence ecosystem, Groen said, building on the work each respective service is doing.

One outstanding challenge that the JAIC faces in that mission is making sure that data used for war fighting is trusted.

It has to be a trusted ecosystem, meaning we actually have to know if were going to bring data into a fires capability, we have to know that thats good data, Groen said. So ... how do we build an ecosystem so that we can know the provenance of data, we can ensure that the algorithms are tested to a satisfactory way, that we can comfortably and safely integrate data and decision-making across war-fighting functions?

Read the original post:
How the Defense Department wants to measure the success of its AI hub - C4ISRNet

Research papers come out far too rapidly for anyone to read them all, especially in the field of machine learning, which now affects (and produces papers in) practically every industry and company. This column aims to collect the most relevant recent discoveries and papers particularly in but not limited to artificial intelligence and explain why they matter.

This week has a bit more basic research than consumer applications. Machine learning can be applied to advantage in many ways users benefit from, but its also transformative in areas like seismology and biology, where enormous backlogs of data can be leveraged to train AI models or as raw material to be mined for insights.

Were surrounded by natural phenomena that we dont really understand obviously we know where earthquakes and storms come from, but how exactly do they propagate? What secondary effects are there if you cross-reference different measurements? How far ahead can these things be predicted?

A number of recently published research projects have used machine learning to attempt to better understand or predict these phenomena. With decades of data available to draw from, there are insights to be gained across the board this way if the seismologists, meteorologists and geologists interested in doing so can obtain the funding and expertise to do so.

The most recent discovery, made by researchers at Los Alamos National Labs, uses a new source of data as well as ML to document previously unobserved behavior along faults during slow quakes. Using synthetic aperture radar captured from orbit, which can see through cloud cover and at night to give accurate, regular imaging of the shape of the ground, the team was able to directly observe rupture propagation for the first time, along the North Anatolian Fault in Turkey.

The deep-learning approach we developed makes it possible to automatically detect the small and transient deformation that occurs on faults with unprecedented resolution, paving the way for a systematic study of the interplay between slow and regular earthquakes, at a global scale, said Los Alamos geophysicist Bertrand Rouet-Leduc.

Another effort, which has been ongoing for a few years now at Stanford, helps Earth science researcher Mostafa Mousavi deal with the signal-to-noise problem with seismic data. Poring over data being analyzed by old software for the billionth time one day, he felt there had to be better way and has spent years working on various methods. The most recent is a way of teasing out evidence of tiny earthquakes that went unnoticed but still left a record in the data.

The Earthquake Transformer (named after a machine-learning technique, not the robots) was trained on years of hand-labeled seismographic data. When tested on readings collected during Japans magnitude 6.6 Tottori earthquake, it isolated 21,092 separate events, more than twice what people had found in their original inspection and using data from less than half of the stations that recorded the quake.

Image Credits: Stanford University

The tool wont predict earthquakes on its own, but better understanding the true and full nature of the phenomena means we might be able to by other means. By improving our ability to detect and locate these very small earthquakes, we can get a clearer view of how earthquakes interact or spread out along the fault, how they get started, even how they stop, said co-author Gregory Beroza.

Original post:
Deep Science: Using machine learning to study anatomy, weather and earthquakes - TechCrunch

C3.ai (NYSE:AI) was one of the hottest tech IPOs of 2020. The enterprise artificial intelligence company priced its IPO at $42 a share on Dec. 8, but the stock opened at $100 the following day and subsequently surged to about $140.

C3.ai raised $651 million in its IPO, and it now has a market cap of about $13.4 billion, or 85 times its fiscal 2020 revenue. That frothy valuation indicates investors are still thrilled about C3.ai's growth prospects -- but the bulls are ignoring some obvious weaknesses, and pricing too much growth into this high-flying stock.

C3.ai's founder and CEO is Thomas Siebel, who previously co-founded Siebel Systems, the enterprise software company Oracle (NYSE:ORCL) acquired for $5.85 billion in2006.

Image source: Getty Images.

Siebel founded C3.ai in 2009. The company initially offered its cloud-based AI tools to energy companies, but it now serves a wide range of organizations across the commercial, industrial, and government sectors.

C3.ai's top customers include the machinery maker Caterpillar, the oil and gas services giant Baker Hughes (NYSE:BKR), and the European energy company Engie (OTC:ENGIY). It notably generated 36% of its revenue from Baker Hughes and Engie in fiscal 2020, which ended in April.

These organizations all use C3.ai's software to streamline their operations, cut costs, and make data-driven decisions. Its software helps Caterpillar optimize its inventories, Baker Hughes streamline its maintenance routines, and Engie modernize its energy infrastructure.

C3.ai expands via a "lighthouse" strategy, in which it secures a top "lighthouse" customer in a sector to attract its industry peers. These lighthouse customers include 3M, Royal Dutch Shell, and the U.S. Air Force.

C3.ai generated 86% of its revenue from subscriptions and the rest from professional services last year. Its revenue rose 88% in 2018, 48% in 2019, and another 71% to $157 million in fiscal 2020. But in the first quarter of 2021, its revenue only rose 16% year over year to $40.5 million as COVID-19 disruptions throttled its growth.

Image source: Getty Images.

C3.ai says it generates "uncommonly high" contract values, thanks to the "high-value outcomes" its AI tools produce. As a result, its average contract was worth $12.1 million in fiscal 2020, which the company calls a "high-water mark for the applications software industry."

C3.ai tries to grow its revenue per customer with a "land and expand" strategy, wherein it locks in customers with a smaller contract, then signs them onto additional contracts. Its initial contract is worth about $13 million, but it believes it can boost that figure to $39 million via additional contracts. Its average contract lasts for about three years.

But like many other cloud service companies, C3.ai is unprofitable. Its net losses widened over the past three years, and it ended 2020 with a net loss of $69.4 million -- compared to a loss of $33.3 million in 2019. It generated a slim profit of $150,000 in the first quarter of 2021, due to lower operating costs during the pandemic, but it probably won't stay in the black for the rest of the year.

C3.ai's customer concentration is a major risk, and it could still face competition from public cloud leaders like Amazon (NASDAQ:AMZN) Web Services (AWS) and Microsoft (NASDAQ:MSFT) Azure, even though it classifies these tech giants as technological partners.

C3.ai's AI services run on top of AWS, Azure, and other cloud platforms -- but AWS and Azure also offer their own integrated AI services. C3.ai claims its services are cheaper, more efficient, and more customizable than those integrated AI solutions, but Amazon and Microsoft could still develop new AI services to compete against C3.ai in the future.

C3.ai has a promising business model, and it could have plenty of room to grow. It estimates the total addressable market for AI tools will grow from $174 billion in 2020 to $271 billion in 2024 -- and its "land and expand" strategy could boost the average values of its contracts as that market grows.

Unfortunately, C3.ai's stock is simply too hot to handle at 85 times last year's sales. Even if it doubles its revenue this year, it would still be pricier than other bubbly tech stocks like Palantir and JFrog -- which both trade at roughly 30 times next year's sales.

I'd consider buying C3.ai's stock if a market crash cuts its price in half, but there's far too much optimism baked in at these prices. The market's near-term momentum might carry it slightly higher, but I'm not interested in paying the wrong price for the right company.

Here is the original post:
Here's Why This Hot Artificial Intelligence IPO Stock Isn't Worth Buying - Motley Fool

Artificial Intelligence

As we wrap up the year, Ill start by pointing out something obvious to anyone who reads my column: Ive rarely mentioned the Covid-19 pandemic. While it has had a major impact on many areas of society, I dont feel that it has changed the artificial intelligence (AI) and machine learning (ML) markets in a significant way. At most, it has accelerated the adoption of the technology, but it hasnt done much. While I hope everyone remains as safe as possible, it is not something in my coverage area. My best wishes to all readers remain.

At the end of 2019, my similar column mentioned retail uses of vision and chatbots being more integrated into other applications during 2020. That happened and chatbots have now become a must have in most customer interaction interfaces. That has meant a more minimal coverage this year. Retail use of AI vision continues to expand, but in the opposite way of the acceleration mentioned in the first paragraph. The pandemic caused slowdown in retail means spending has slowed while survival is at stake. I expect to see that pick up again in the second half of 2021, as the vaccines become more widespread and consumer confidence improves.

What was exciting about 2020 was the clear evidence of AI tools moving out of the standard worlds of core enterprise data analysis, marketing, retail vision and facial recognition. Two areas discussed in multiple columns were in infrastructure.

The first of those areas is in the cable industry. The large ISPs are focused on better analyzing and optimizing their networks. Smaller companies have come along and are helping the large firms by beginning to use AI at the edge, enhancing modems and routers to better analyze where problems are originating in households. That will improve the experience both for households and the internet providers.

The second was a bundle of segments around facilities. AI is being used to enhance everything from planning through construction and maintenance. One of the more intriguing aspects was the use vision and ML to help businesses and government analyze physical structures in the real world. In the construction arena, AI vision and analysis are helping to better manage and schedule projects.

Discussions with a few vendors shows that artificial intelligence is also working its way into different areas of the sales process. This is a slower change, only adding a bit more accuracy to sales systems in enterprise sales. That will continue, but the real advances I see are coming from the more commoditized markets. Earlier this month, I described the complexity of the commodity channels and sales policies. In this area, I see more visible and rapid adoption of AI to better optimize the channels, while enterprise sales will see a more steady, gradual adoption that is still important.

When looking to 2020 and the future, I still see governments moving slowly. Thats not a surprise. The USA, plenty of other nations, and the EU, have been putting out policy statements, but thats really all there is. A few statements by people in Congress have shown an increasing interest in the subject, and the Bipartisan Policy Center is looking at it, but still having an Inside the Beltway view of the issue.

Departments are beginning to look at the issue. As healthcare has been an early adopter of AI, especially in radiology, the FDA has begun to look at the issue. A number companies mentioned that the FDA is looking at how to adapt policies to manage changing algorithms, as usual the organization itself isnt forthcoming. Talking to anyone in the FDA involved in defining the process was not possible, all that was sent to me was vague statements. Seeing how that evolves in the coming year will be interesting.

On the other hand, NGOs are also busy pushing out opinions, policy statements and books. In 2020, Ive discussed information from the Brookings Institution and the World Economic Forum (WEF). Brookings has some interesting things going on and Ill continue to watch the evolution of their AI understanding and views. The WEF, being a business group, has almost completely avoided mentioning governments at all. Theyve put out general statements about business ethics with AI, but the regulatory environment will be changing. I dont expect to see anything significant next year, but I would hope theres momentum building for action in 2022.

I have constantly returned to the refrain that AI is not a panacea, its a tool. One thing preventing wider adoption of the tool is that development software is moving more slowly than I hoped. There have been some user interface changes, with the basics of a graphical user interface (GUI) being layered on some aspects of the development cycle, but theres still a long way to go.

There needs to be a change similar to that between Third Generation programming languages to Fourth Generation languages, where coding was minimized, and graphical tools made it easier for less technical personnel to build applications supporting their business needs.

One trend I see helping that is that the importance of data privacy has filtered down to the developers. Data cleansing has usually been, sadly, an afterthought. As it becomes more important, and ML uses larger bodies of data, the need to quickly manage that data will drive UI changes. That will hopefully bubble up from managing privacy, to analyzing features for selection, to higher level management of the development process.

Frameworks, such as TensorFlow, as still the main tool for developing ML engines. That requires both more time and more money, as they are complex and require more knowledge and a higher price tag for programmers. While schools are working to meet the demand, theres also a need to increase the supply by providing more abstract tools that allow more people to leverage the power and promise of AI/ML.

Go here to see the original:
The Obligatory Artificial Intelligence Year End Article, 2020 Edition - Forbes

What better time than now to unveil what to look out for in the world of AI and technology in 2021.

By Prithwis De

The year 2020 will be marked as an unprecedented year in history due to the adverse impact of coronavirus worldwide. This pandemic has started bringing extraordinary changes in some key areas. The trends of faster drug development, effective remote care, efficient supply chain, etc, will continue into 2021. Drone technology is already playing a vital role in delivering food and other essentials alongside relief activities.

With Covid-19 came a new concept of the Internet of Behaviour within organisations to track human behaviour in the work environment and trace any slack in maintaining guidelines. Now on, organisations are set to capture and combine behaviour-related data from different sources and use it. We can assertively say it will affect the way organisations interact with people, going forward. Students are experiencing distance learning, taking examinations under remotely-monitored and proctored surveillance systems through identity verification and authentication in real time.

All these will have a high impact on technology, which will shape our outlook in the future. Businesses around the globe are taking the giant leap to become tech-savvy with quantum computing, artificial intelligence (AI), cybersecurity, etc. AI and cloud computing are alluring us all towards an environment of efficiency, security, optimisation and confidence. What better time than now to unveil what to look out for in the world of AI and technology in 2021.

What 2020 has paved the way for is quantum computing. Now, be prepared to adapt to a hybrid computing approach (conventional cum quantum computing) to problem-solving. This paradigm shift in computing will result in the emergence of implausible ways to solve existing business problems and ideate new opportunities. Its effects will be visible on our ability to perform better in diverse areasfinancial forecasting, weather predictions, drug and vaccine development, blood-protein analysis, supply chain planning and optimisation, etc. Quantum Computing as a Service (QCaaS) will be a natural choice for organisations to plug into the experiments as we advance. Forward-thinking businesses are excited to take the quantum leap, but the transition is still in a nascent stage. This new year will be a crucial stepping stone towards the future of things to change in the following years.

Cloud providers such as Amazon (AWS), Microsoft (Azure) and Google will continue to hog the limelight as the AI tool providers for most companies leaning towards real-time experiments in their business processes in the months to follow. Efficiency, security and customisation are the advantages for which serverless and hybrid cloud computing are gaining firm ground with big enterprises. It will continue to do so in 2021.

Going forward, the aim is to make the black box of AI transparent with explainable AI. The lack of clarity hampers our ability to trust AI yet. Automated machine learning (AutoML), another crucial area, is likely to be very popular in the near future. One more trend that caught on like wildfire in 2020 is Machine Learning Operations (MLOps). It provides organisations visibility of their models and has become an efficient tool to steer clear of duplicated efforts in AI. Most of the companies have been graduating from AI experimentations and pilot projects to implementation. This endeavour is bound to grow further and enable AI experts to have more control over their work from end-to-end now onwards.

Cybersecurity will gain prime importance in 2021 and beyond as there is no doubt that hacking and cybercrime prevention are priorities for all businesses with sensitive data becoming easily accessible with advanced phishing tools. Advanced prediction algorithms, along with AI, will play a decisive role in the future to prevent such breaches in data security.

AI and the Internet of Things along with edge computing, which is data processing nearer the source closer to the device at the edge of the network, will usher in a new era for actionable insights from the vast amount of data. The in-memory-accelerated-real-time AI will be needed, particularly when 5G has started creating new opportunities for disruption.

In 2020, there was a dip in overall funding as the pandemic had badly impacted the investment sector due to a reduction in activity. Some of the technology start-ups are still unable to cope up with the challenges created due to Covid-19 and the consequent worsening economic conditions. According to NASSCOM, around 40% of Indian start-ups were forced to stop their operations. In 2021, mergers and acquisitions of start-ups are expected. The larger companies are likely to target smaller companies, specialised mainly in niche and innovative areas such as drug development, cybersecurity, AI chips, cloud computing, MLOps, etc.

The businesses in 2021 and beyond will develop into efficient workplaces for everybody who believes in the power of technology. It is important to bear in mind that all trends are not necessarily independent of each other, but rather form the support base of the other as well as work in tandem with human intervention. So, are the hybrid trends and solutions here to stay for the next few years for the smooth running of various organisations? Only time will tell. But the need for AI and newer technology adoption and modernisation increases manifold.

The author is an analytics and AI professional, based in London, working in a big IT company. Views are personal

Get live Stock Prices from BSE, NSE, US Market and latest NAV, portfolio of Mutual Funds, Check out latest IPO News, Best Performing IPOs, calculate your tax by Income Tax Calculator, know markets Top Gainers, Top Losers & Best Equity Funds. Like us on Facebook and follow us on Twitter.

Financial Express is now on Telegram. Click here to join our channel and stay updated with the latest Biz news and updates.

Go here to read the rest:
Tech trends in 2021: How artificial intelligence and technology will reshape businesses - The Financial Express

The newly added research report is a holistic guide to understand various other elements that play crucial role in growth progression.

The report serves as a brilliant investment guide to deliver core information on diverse market elements such as customer motivators, customer response and behavior, brand developments and positioning across the competitive isle. The report is designed to aid the investment decisions and motivate crucial investment discretion for new businesses looking out for seamless market penetration.

Access the PDF sample of the Cognitive System and Artificial Intelligence (AI) Systems market report @ https://www.orbisresearch.com/contacts/request-sample/2481605?utm_source=Atish

The report is focused at delivering a high-end replication of all major growth facets across the competition ecosystem in order to ensure ample competitive edge for inquisitive market participants. Highly interested market participants and established international vendors may refer to this research report to design and deploy agile investment decisions.

The key players covered in this studyGoogleBaiduIBMMicrosoftSAPIntelSalesforceBrighterionKITT.AIIFlyTekMegvii TechnologyAlbert TechnologiesH2O.aiBrainasoftYseopIpsoftNanoRep(LogMeIn)Ada SupportAstute SolutionsIDEAL.comWiproMarket analysis by product typeOn-PremiseCloud-basedMarket analysis by marketVoice ProcessingText ProcessingImage ProcessingMarket analysis by RegionUnited StatesEuropeChinaJapanSoutheast AsiaIndiaCentral & South AmericaThe study objectives of this report are:To analyze global Cognitive System & Artificial Intelligence (AI) Systems status, future forecast, growth opportunity, key market and key players.To present the Cognitive System & Artificial Intelligence (AI) Systems development in United States, Europe and China.To strategically profile the key players and comprehensively analyze their development plan and strategies.To define, describe and forecast the market by product type, market and key regions.In this study, the years considered to estimate the market size of Cognitive System & Artificial Intelligence (AI) Systems are as follows:History Year: 2018-2019Base Year: 2018Estimated Year: 2019Forecast Year 2019 to 2025For the data information by region, company, type and application, 2018 is considered as the base year. Whenever data information was unavailable for the base year, the prior year has been considered.

Understanding Segmentation:

The report in its subsequent sections includes elements of vendor profiles, manufacturer activities as well as stakeholder investments that tend to indicate high potential growth in global Cognitive System and Artificial Intelligence (AI) Systems market.The primary motive of this exclusive insider report on global Cognitive System and Artificial Intelligence (AI) Systems market hovers over underpinning crucial report elements pertaining to regional expanse that the market retains at a global platform and the various implications of vendor initiatives upon these regions that collectively reflect healthy growth trajectory.

Make an enquiry of Cognitive System and Artificial Intelligence (AI) Systems market report @ https://www.orbisresearch.com/contacts/enquiry-before-buying/2481605?utm_source=Atish

Artificial intelligence (AI, also machine intelligence, MI) is intelligence displayed by machines, in contrast with the natural intelligence (NI) displayed by humans and other animals. Artificial intelligence software is Software that is capable of intelligent behavior. In creating intelligent software, this involves simulating a number of capabilities, including reasoning, learning, problem solving, perception, and knowledge representation.AI and Cloud-based increasingly will be embedded into everyday things such as appliances, speakers and hospital equipment. This phenomenon is closely aligned with the emergence of conversational systems, the expansion of the IoT into a digital mesh and the trend toward digital twins.In 2018, the global Cognitive System & Artificial Intelligence (AI) Systems market size was xx million US$ and it is expected to reach xx million US$ by the end of 2025, with a CAGR of xx% during 2019-2025.This report focuses on the global Cognitive System & Artificial Intelligence (AI) Systems status, future forecast, growth opportunity, key market and key players. The study objectives are to present the Cognitive System & Artificial Intelligence (AI) Systems development in United States, Europe and China.The key players covered in this studyGoogleBaiduIBMMicrosoftSAPIntelSalesforceBrighterionKITT.AIIFlyTekMegvii TechnologyAlbert TechnologiesH2O.aiBrainasoftYseopIpsoftNanoRep(LogMeIn)Ada SupportAstute SolutionsIDEAL.comWiproMarket analysis by product typeOn-PremiseCloud-basedMarket analysis by marketVoice ProcessingText ProcessingImage ProcessingMarket analysis by RegionUnited StatesEuropeChinaJapanSoutheast AsiaIndiaCentral & South AmericaThe study objectives of this report are:To analyze global Cognitive System & Artificial Intelligence (AI) Systems status, future forecast, growth opportunity, key market and key players.To present the Cognitive System & Artificial Intelligence (AI) Systems development in United States, Europe and China.To strategically profile the key players and comprehensively analyze their development plan and strategies.To define, describe and forecast the market by product type, market and key regions.In this study, the years considered to estimate the market size of Cognitive System & Artificial Intelligence (AI) Systems are as follows:History Year: 2018-2019Base Year: 2018Estimated Year: 2019Forecast Year 2019 to 2025For the data information by region, company, type and application, 2018 is considered as the base year. Whenever data information was unavailable for the base year, the prior year has been considered.

Artificial intelligence (AI, also machine intelligence, MI) is intelligence displayed by machines, in contrast with the natural intelligence (NI) displayed by humans and other animals. Artificial intelligence software is Software that is capable of intelligent behavior. In creating intelligent software, this involves simulating a number of capabilities, including reasoning, learning, problem solving, perception, and knowledge representation.AI and Cloud-based increasingly will be embedded into everyday things such as appliances, speakers and hospital equipment. This phenomenon is closely aligned with the emergence of conversational systems, the expansion of the IoT into a digital mesh and the trend toward digital twins.In 2018, the global Cognitive System & Artificial Intelligence (AI) Systems market size was xx million US$ and it is expected to reach xx million US$ by the end of 2025, with a CAGR of xx% during 2019-2025.This report focuses on the global Cognitive System & Artificial Intelligence (AI) Systems status, future forecast, growth opportunity, key market and key players. The study objectives are to present the Cognitive System & Artificial Intelligence (AI) Systems development in United States, Europe and China.The key players covered in this studyGoogleBaiduIBMMicrosoftSAPIntelSalesforceBrighterionKITT.AIIFlyTekMegvii TechnologyAlbert TechnologiesH2O.aiBrainasoftYseopIpsoftNanoRep(LogMeIn)Ada SupportAstute SolutionsIDEAL.comWiproMarket analysis by product typeOn-PremiseCloud-basedMarket analysis by marketVoice ProcessingText ProcessingImage ProcessingMarket analysis by RegionUnited StatesEuropeChinaJapanSoutheast AsiaIndiaCentral & South AmericaThe study objectives of this report are:To analyze global Cognitive System & Artificial Intelligence (AI) Systems status, future forecast, growth opportunity, key market and key players.To present the Cognitive System & Artificial Intelligence (AI) Systems development in United States, Europe and China.To strategically profile the key players and comprehensively analyze their development plan and strategies.To define, describe and forecast the market by product type, market and key regions.In this study, the years considered to estimate the market size of Cognitive System & Artificial Intelligence (AI) Systems are as follows:History Year: 2018-2019Base Year: 2018Estimated Year: 2019Forecast Year 2019 to 2025For the data information by region, company, type and application, 2018 is considered as the base year. Whenever data information was unavailable for the base year, the prior year has been considered.

The report diversifies the global geographical expanse of Cognitive System and Artificial Intelligence (AI) Systems market into five prominent regions such as Europe, APAC, MEA, North and South America.

Browse the complete Cognitive System and Artificial Intelligence (AI) Systems market report @ https://www.orbisresearch.com/reports/index/global-cognitive-system-and-artificial-intelligence-ai-systems-market-size-status-and-forecast-2019-2025?utm_source=Atish

The implications of various investment decisions maneuvered by protuberant manufacturers across various regional pockets have been minutely addressed in this versatile report. Details pertaining to country-wise developments along with immersive details on broad regional clusters have been showcased with prominent geographical enclosures such as enlisted as under:

About Us:Orbis Research (orbisresearch.com) is a single point aid for all your market research requirements. We have vast database of reports from the leading publishers and authors across the globe. We specialize in delivering customized reports as per the requirements of our clients. We have complete information about our publishers and hence are sure about the accuracy of the industries and verticals of their specialization. This helps our clients to map their needs and we produce the perfect required market research study for our clients.

Contact Us:Hector CostelloSenior Manager Client Engagements4144N Central Expressway,Suite 600, Dallas,Texas 75204, U.S.A.Phone No.: +1 (972)-362-8199 ; +91 895 659 5155

https://farmingsector.co.uk/

Here is the original post:
Cognitive System and Artificial Intelligence (AI) Systems Market 2020: Industry Growth, Competitive Analysis, Future Prospects and Forecast 2025 -...

The usage of recommendation engines is growin in consumer services. Be it Spotify, Netflix, or Amazon; brands are leveraging artificial intelligence-based recommendation systems to provide more personalized services and enhance user experience. Apps like Google Maps and UBER also rely on AI to provide accurate directions and estimated travel time, respectively. So, it is obvious that many of us depend on AI to make numerous daily life decisions. However, will artificial intelligence-based recommendations pass the litmus test when fared against human recommendations, against the backdrop of concerns against it?

A person or machine makes a recommendation after learning their preferences. Basically, after filtering through varied information, suggestions are made by tailoring data to users interests, preferences, or behavioral history on an item.

There are several instances when artificial intelligence has been alleged as biased when making a suggestion or recommendation. For instance, few years ago, Reuters reported that the e-commerce giant Amazon.com Incssecret AI recruiting tool showed bias against women. The software penalized applicants who attended all-womens colleges, as well as any resumes that contained the word womens. Bias has also been observed in facial recognition algorithms that tend to misidentify people due to their gender or race. These biases may have existed because of the presence of bias in the training dataset or faulty programming. This also brings us to another major concern regarding artificial intelligence, i.e. the black box problem.

Though it is argued that AI-based decisions tend to be logical and adheres specified set of rules, we arent sure of how those decisions are made. Fortunately, to counter this, researchers have proposed Explainable AI (XAI), fine-tuning, unmasking AI, and more. Addressing the black box issue is important to understand the cause of the mistake or bias, or decision made by artificial intelligence models, boost transparency, and tweak it later. Recently researchers at Duke University proposed a method that targets reasoning process behind the AI predictions and recommendations.

When pitted against recommendations by humans, AI need not necessarily always have a win-win situation. It is true that data-driven recommendationsare always preferred; however, the preferences to accept humans and artificial intelligence based recommendations differ with respect to situation and use case. It all stems from the word-of-machine effect.

Recently, an article on When Do We Trust AIs Recommendations More Than Peoples? by University of Virginias Darden Business School Professor Luca Cian and Boston Universitys Questrom School of Business Professor Chiara Longoni, was published in the Harvard Business Review. In the article, they explained this phenomena asa widespread belief that AI systems are more competent than humans in dispensing advice when utilitarian qualities are desired and are less competent when the hedonic qualities are desired.

The article authors clarify that, it doesnt imply that artificial intelligence is competent than humans at assessing and evaluating hedonic attributes nor are humans in the case of utilitarian attributes. As per their experiment results, suppose someone is focused on utilitarian and functional qualities, from a marketers perspective, the word of a machine is more effective than the word of human recommenders. For someone focused on experiential and sensory qualities, human recommenders are more effective.

Out of one of the 10 studies by Cian and Longoni, one study involved recruiting 144 participants from the University of Virginia campus and informing them about testing chocolate-cake recipes for a local bakery. During the experiment, the participants were offered two options: one cake created with ingredients selected by an AI chocolatier and one created with ingredients selected by a human chocolatier. Both cakes were identical in appearance and ingredients. Participants were asked to eat the cakes and rate them on the basis of two experiential/sensory features (indulgent taste and aroma, pleasantness to the senses) and two utilitarian/functional attributes (beneficial chemical properties and healthiness). It was observed that while participants found the AI recommended cake less tasty than human recommended one, yet it was healthier than the other.

Longoni and Cian also assert that consumers will embrace artificial intelligence recommendations if they believe a human was part of the recommendation process.

The human brain has an edge over AI for its cognitive skills. It acquires knowledge and improves reasoning by learning from experience, abstract concepts, several cognitive processes, and more, its ability to manipulate ones environment. Whereas, artificial intelligence models try to mimic human intelligence by following certain program rules and continuous self-learning (machine learning). Regardless of their learning method, both are capable of giving good and bad recommendations. Second, people nowadays are slowly starting to trust AI. Independent surveys have found that people may opt for AI for higher flexibility and control. Respondents believe that the relationship between humans and AI and trust will likely improve in the future, given that AI proves itself safe, and transparent.

As recommendations become an effective marketing tool, developers and marketers have to be careful in leveraging artificial intelligence algorithms. They can program the AI system to identify what the customer is actually looking for, before making any suggestion. As AI becomes more tangible than ever, its ability to offer recommendations that are unique and personal in nature will increase too. It is true that currently, AI lacks quick thinking, creativity and other attributes associated with human intelligence, but with innovations around the clock, who knows what AI will be capable of in the future. At the same time, humans and AI can live in a symbiotic or collaborative relation to avail of each others benefits.

Share This ArticleDo the sharing thingy

See the rest here:
Recommendations by Artificial Intelligence vs Humans: Who will win? - Analytics Insight

Artificial intelligence (AI) has captivated the imagination of science-fiction movie audiences for many years and has been used in the upstream oil and gas industry for more than a decade (Mohaghegh 2005, 2011). But few industries evolve more quickly than those from Silicon Valley, and it accordingly follows that the technology has grown and changed considerably since this discussion began. The oil and gas industry, therefore, is at a point where it would be prudent to take stock of what has been achieved with AI in the sector, to provide a sober assessment of what has delivered value and what has not among the myriad implementations made so far, and to figure out how best to leverage this technology in the future in light of these learnings.

When one looks at the long arc of AI in the oil and gas industry, a few important truths emerge. First among these is the fact that not all AI is the same. There is a spectrum of technological sophistication. Hollywood and the media have always been fascinated by the idea of artificial superintelligence and general intelligence systems capable of mimicking the actions and behaviors of real people. Those kinds of systems would have the ability to learn, perceive, understand, and function in human-like ways (Joshi 2019). As alluring as these types of AI are, however, they bear little resemblance to what actually has been delivered to the upstream industry. Instead, we mostly have seen much less ambitious narrow AI applications that very capably handle a specific task, such as quickly digesting thousands of pages of historical reports (Kimbleton and Matson 2018), detecting potential failures in progressive cavity pumps (Jacobs 2018), predicting oil and gas exports (Windarto et al. 2017), offering improvements for reservoir models (Mohaghegh 2011), or estimating oil-recovery factors (Mahmoud et al. 2019).

View post:
Artificial Intelligence in the E&P Industry: What Have We Learned So Far and Where to Next? - Journal of Petroleum Technology

Mumbai, 4 Jan 2020: Covestro is driving forward the use of artificial intelligence (AI) in the company as well as throughout the entire chemical industry. As part of these efforts, the Leverkusen-based materials manufacturer is conducting several pilot projects across different divisions to investigate how digital technologies can make processes more efficient and sustainable.

With the help of artificial intelligence, we will be able to find digital solutions across departmental boundaries in the future that were not possible before. This opens up completely new possibilities to achieve greater sustainability with improved resource utilization and thus to serve the needs of our customers even more precisely, says Sucheta Govil, Chief Commercial Officer of Covestro.

Pilot projects put the use of AI to the test

At its Dormagen production site, Covestro is testing how the manufacturing process for polyesters can be optimized. Polyesters are needed for the production of polyurethanes. The use of artificial intelligence in the processing of the companys comprehensive process data is intended to create free production capacities and minimize energy consumption. Digital technologies are also used in Dormagen and at the Leverkusen and Krefeld-Uerdingen sites to predict the peak steam consumption of production plants. In this way, energy consumption and costs can be reduced in the long term.

Another project deals with the digital customer experience. Machine learning is used to help to identify potential customers for Covestro at all digital touchpoints on the Internet and social media. The aim is to win them over as customers in the long term through consultation and information. Artificial intelligence will also be used in Covestros purchasing department in the future. Faulty invoices can then be identified with less effort, making the invoicing acknowledgment process much more efficient.

Utilizing data science for more intelligent production

AI is a fascinating opportunity for Covestro worldwide. The ongoing projects show that data and its proper use not only make the entire industry smarter and faster but can also contribute to the bottom line, says Nils Janus, Head of Advanced Analytics at Covestro. To make optimum use of data-based findings in the future, the materials manufacturer has developed the Covestro Analytics Platform (CAP), a platform for data scientists. It collects raw data from production plants, research results, and business processes and supplements it with external databases to perform analyses and train machine learning models. In this way, Covestro is taking another important step towards digitalization and data-driven business management.

More here:
Artificial Intelligence To Make The Chemical Industry Faster And Smarter - Punekar News