Robotics and AI are not panaceas for solving labor shortages or other product-related problems. But in the next decade, robots and crop-support software are going to take certain tasks out of the hands of the high-tech greenhouse.

Growers are not going to produce more cheaply because of robotization and digitization, but mostly differently. Managing larger and more international companies will become easier, management skills will change, and closer cooperation with suppliers and customers will be possible. Pests can also be dealt with earlier, more sustainably, or more precisely, or the quality of the final product can be improved. All of this has a great potential value that is difficult to assess.

Lack of labor and the cost of labor are major drivers of robotization. The knowledge and skills of horticultural companies are indispensable in developing robots and AI. For robotics players, connecting with the right growers and quickly adapting products based on experience, service, and convenience are examples of success factors.

For growers, there are several options for applying innovations in robotics and AI. From co-investing in the companies to cautiously trying via a (trial) subscription.

In the future, an entirely different automated growing system is a smarter solution for certain crops. But this requires substantial investments and many adjustments, e.g., in varieties. Robotization and digitization also have disadvantages. Consider the growing dependence on large software companies, cyber risks, less flexibility, negative consumer perception, and less diversity of companies and products.

Read the report (in Dutch) here.

For more information:Rabobankwww.rabobank.com

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Using robots and artificial intelligence in greenhouse horticulture - hortidaily.com

NEW YORK, Oct. 4, 2022 /PRNewswire/ -- The artificial intelligence (AI) market size in the BFSI sector is set to grow by $32.97 bn between 2021 and 2026, progressing at a CAGR of 36.68%. According to Technavio, the market is fragmented, and the degree of fragmentation will accelerate during the forecast period. As market growth over the next five years is expected to remain high, the competitive rivalry among market vendors will remain limited. To know more about the vendor landscape Read Sample PDF Report Before Purchasing.

Technavio has announced its latest market research report titled Global Artificial Intelligence (AI) Market in BFSI Sector 2022-2026

The report identifies Alphabet Inc., Amazon.com Inc., Amelia US LLC, Baidu Inc, Glia Technologies Inc, Inbenta Technologies Inc., Intel Corp., International Business Machines Corp., Lexalytics Inc., Microsoft Corp., NVIDIA Corp., Oracle Corp., Palantir Technologies Inc., Salesforce Inc., ServiceNow Inc., Verint Systems Inc, ZestFinance Inc, and SAP SEare some of the major market participants. Although the Enhanced operational efficiency with AI will offer immense growth opportunities, the need for high data quality will challenge the growth of the market participants. To make the most of the opportunities, market vendors should focus more on the growth prospects in the fast-growing segments, while maintaining their positions in the slow-growing segments.

Artificial Intelligence (AI) Market in BFSI Sector 2022-2026: Segmentation

Artificial Intelligence (AI) Market in BFSI Sector is segmented as below:

The artificial intelligence (AI) market share growth in the BFSI sector by the banking segment will be significant during the forecast period. The use of cognitive technology, along with AI, helps banks to leverage digitalization and sustain competition with FinTech players. AI technologies are revolutionizing banking processes and the relationship between banks and customers. AI is expected to shape the future of the banking sector as it provides the power of advanced data analytics to fight against fraudulent transactions and improve compliance, all within seconds.

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48% of the market's growth will originate from North America during the forecast period. The early adoption and increasing investments in AI technologies by players such as IBM, Google, Microsoft, and AWS in the region will facilitate the artificial intelligence (AI) market growth inBFSI sector in North America over the forecast period. This market research report entails detailed information on the competitive intelligence, marketing gaps, and regional opportunities in store for vendors, which will assist in creating efficient business plans. Our artificial intelligence (ai) market in BFSI sector report covers the following areas:

Artificial Intelligence (AI) Market in BFSI Sector 2022-2026: Vendor Analysis

We provide a detailed analysis of around 25 vendors operating in the Artificial Intelligence (AI) Market in BFSI Sector, including some of the vendors such as vendors Backed with competitive intelligence and benchmarking, our research reports on the Artificial Intelligence (AI) Market in BFSI Sector are designed to provide entry support, customer profile and M&As as well as go-to-market strategy support.

Artificial Intelligence (AI) Market in BFSI Sector 2022-2026: Key Highlights

CAGR of the market during the forecast period 2022-2026

Detailed information on factors that will assist artificial intelligence (AI) market in BFSI sector growth during the next five years

Estimation of the artificial intelligence (AI) market in BFSI sector size and its contribution to the parent market

Predictions on upcoming trends and changes in consumer behavior

The growth of the artificial intelligence (AI) market in BFSI sector

Analysis of the market's competitive landscape and detailed information on vendors

Comprehensive details of factors that will challenge the growth of the artificial intelligence (AI) market in BFSI sector vendors.

Related Reports:

Artificial Intelligence (AI) Market In BFSI Sector Scope

Report Coverage

Details

Page number

120

Base year

2021

Forecast period

2022-2026

Growth momentum & CAGR

Accelerate at a CAGR of 36.68%

Market growth 2022-2026

$32.97 billion

Market structure

Fragmented

YoY growth (%)

33.99

Regional analysis

North America, APAC, Europe, Middle East and Africa, and South America

Performing market contribution

North America at 48%

Key consumer countries

US, Canada, China, Japan, and UK

Competitive landscape

Leading companies, competitive strategies, consumer engagement scope

Companies profiled

Alphabet Inc., Amazon.com Inc., Amelia US LLC, Baidu Inc, Glia Technologies Inc, Inbenta Technologies Inc., Intel Corp., International Business Machines Corp., Lexalytics Inc., Microsoft Corp., NVIDIA Corp., Oracle Corp., Palantir Technologies Inc., Salesforce Inc., ServiceNow Inc., Verint Systems Inc, ZestFinance Inc, and SAP SE

Market Dynamics

Parent market analysis, Market growth inducers and obstacles, Fast-growing and slow-growing segment analysis, COVID-19 impact and future consumer dynamics, and market condition analysis for the forecast period.

Customization purview

If our report has not included the data that you are looking for, you can reach out to our analysts and get segments customized.

Table Of Contents :

1 Executive Summary

2 Market Landscape

3 Market Sizing

4 Five Forces Analysis

5 Market Segmentation by End-user

6 Customer Landscape

7 Geographic Landscape

8 Drivers, Challenges, and Trends

9 Vendor Landscape

10 Vendor Analysis

11 Appendix

About Us

Technavio is a leading global technology research and advisory company. Their research and analysis focus on emerging market trends and provides actionable insights to help businesses identify market opportunities and develop effective strategies to optimize their market positions. With over 500 specialized analysts, Technavio's report library consists of more than 17,000 reports and counting, covering 800 technologies, spanning across 50 countries. Their client base consists of enterprises of all sizes, including more than 100 Fortune 500 companies. This growing client base relies on Technavio's comprehensive coverage, extensive research, and actionable market insights to identify opportunities in existing and potential markets and assess their competitive positions within changing market scenarios.

Contact

Technavio ResearchJesse MaidaMedia & Marketing ExecutiveUS: +1 844 364 1100UK: +44 203 893 3200Email: media@technavio.comWebsite: http://www.technavio.com/

Global Artificial Intelligence (AI) Market in BFSI Sector 2022-2026

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Artificial Intelligence (AI) Market in BFSI Sector to Record USD 32.97 Billion growth between 2021 and 2026; Major Opportunities with Alphabet Inc....

There's nothing that blurs the line between frightening and fascinatingquitelike artificial intelligence.

And since artificial intelligence and electronic music are becoming moresymbiotic by the day, the staff here at EDM.comwanted to see just how far it could go in a visual sense. So we came up with bizarre descriptions of artists and fed them through A.I. art generators.

Some images are photorealistic. Others arefancifullydistorted. And the majority of them are flat-out creepy.

But we digress. Read on to see our weird and wonderful creations.

"TOKiMONSTA DJing in a neon sky arcade with golden canaries" by Jason Heffler.

Jason Heffler

"REZZ DJ as painted by Salvador Dal" by Nick Yopko.

Nick Yopko

"Flume creating his latest album in a psychedelic jungle" by Koji Aiken.

Koji Aiken

"Martin Garrix in his studio at the top of a cyberpunk skyscraper" by Konstantinos Karakolis.

Konstantinos Karakolis

"CloZee DJs underwater with neon jellyfish"byShakiel Mahjouri.

Shakiel Mahjouri

Charlotte de Witte DJing in a hurricane of music by Tessa Frey.

Tessa Frey

"TroyBoi DJing inside a technicolor candy shop rave"by Carlie Belbin.

Carlie Belbin

"Lane 8 DJing on top of a giant mushroom" by Mikala Lugen.

Mikala Lugen

"Daft Punk baking a cake on the moon" by Jarett Lopez.

Jarett Lopez

Shaq DJing at the base of an active volcano site with lightning in the sky by Cameron Sunkel.

Scroll to Continue

Cameron Sunkel

"Marc Rebillet in a robe in Manhattan singing to a sea of psychedelic flamingos"by Leah McClure.

Leah McClure

Porter Robinson DJing deep in the ocean while gasping for the last sight of sky" by Grecco Costamagna.

Grecco Costamagna

Mija DJing in a post-apocalyptic, punk rock cyberpunk dystopian rave by Brian Rapaport.

Brian Rapoport

"Calvin Harris playing piano on a rowboat in outer space" by Kyle B. Jones.

Kyle B. Jones

"Inside of a tropical coconut, Kygo plays a glittery piano surrounded by glowing flamingos"by Brooke Bierman.

Brooke Bierman

"Dillon Francis and a colorful piatawalking through a futuristic cityscape"- by Lennon Cihak.

Lennon Cihak

Perhaps no artist is more fitting for this A.I. endeavor than the iconic Aphex Twin, whose metaphysically madcap aesthetic chills the collective spine of the music industry to this day. Sowe had a little too much fun.

"Aphex Twin creating his own twisted synthesizer in a dystopian depraved hellscape surrounded by strange dark followers" by Saad Masood.

Saad Masood

Saad Masood

Saad Masood

"Aphex twin performs in a dystopian depraved hellscape for a crowd of dark souls" bySaad Masood.

Saad Masood

Saad Masood

Saad Masood

Editor's Note: The images in this article were generated usingWonder and Dream by WOMBO.

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We Came Up With Bizarre Descriptions of DJsThen Used Artificial Intelligence to Bring Them to Life - EDM.com

DUBLIN--(BUSINESS WIRE)--The "Artificial Intelligence Market in the Education Sector in US 2022-2026" report has been added to ResearchAndMarkets.com's offering.

The artificial intelligence market in the education sector is poised to grow by $374.3 mn during 2022-2026, accelerating at a CAGR of 48.15% during the forecast period. The report on the artificial intelligence market in the education sector provides a holistic analysis, market size and forecast, trends, growth drivers, and challenges, as well as vendor analysis covering around 25 vendors.

The report offers an up-to-date analysis of the current country market scenario, the latest trends and drivers, and the overall market environment. The market is driven by increasing demand for ITS.

The artificial intelligence market in the education sector analysis includes the end-user segment and geographic landscape.

The artificial intelligence market in the education sector is segmented as below:

By End-user

By Type

This study identifies the increased emphasis on chatbots as one of the prime reasons driving the artificial intelligence market in the education sector growth during the next few years. Also, growing emphasis on crowdsourced tutoring and increasing emphasis on content analytics will lead to sizable demand in the market.

The report on the artificial intelligence market in the education sector covers the following areas:

Key Topics Covered:

1 Executive Summary

2 Market Landscape

3 Market Sizing

4 Five Forces Analysis

5 Market Segmentation by End-user

6 Market Segmentation by Type

7 Customer Landscape

8 Drivers, Challenges, and Trends

9 Vendor Landscape

10 Vendor Analysis

11 Appendix

Companies Mentioned

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

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U.S. Artificial Intelligence Market in the Education Sector to Grow by $374.3 Million During 2022-2026 - ResearchAndMarkets.com - Business Wire

DUBLIN--(BUSINESS WIRE)--The "Global Artificial Intelligence in Diabetes Management Market (2022-2027) by Device, Technique, and Geography, with Competitive Analysis, Impact of COVID-19, and Ansoff Analysis" report has been added to ResearchAndMarkets.com's offering.

The Global Artificial Intelligence in Diabetes Management Market is estimated to be worth USD 590.32 million in 2022, and is expected to reach USD 2,235.15 million by 2027, growing at a CAGR of 30.51%.

Market dynamics are forces that impact the prices and behaviors of the 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.

The Competitive Quadrant

The report includes the 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

Why Buy This Report?

Market Dynamics

Drivers

Restraints

Opportunities

Challenges

Market Segmentation

The Global Artificial Intelligence in Diabetes Management Market is segmented based on Device, Technique, and Geography.

Companies Mentioned

For more information about this report visit https://www.researchandmarkets.com/r/33g9eg

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The Worldwide Artificial Intelligence in Diabetes Management Industry is Expected to Reach $2.2 Billion by 2027 - ResearchAndMarkets.com - Business...

Thanks to the internet and social networks, we now have real-time access to global dialogues, trends, and news; by using artificial intelligence to create art based on contemporary debate topics, Woods Art Institutes The Art of Trending project examines the boundaries of art and what it is that makes an artist.

The institute describes it as the most contemporary art exhibition, curated by all of us and executed by AI.

It is impressive and frightening at the same time how far artificial intelligence has come in this area. The many questions that this raises for our industry are what I find particularly fascinating, Managing Director of INGO Hamburg Tobias Ahrans tells Impakter.

Launched in cooperation with creative agency INGO Hamburg and MAGIG Design + Technologies, the campaign began on September 19 on the Woods Art Institutessocial platforms and billboards around Germany. Due to its success, it has been extended and will last until the end of October.

The Art of Trendingsoftware employs hashtags gleaned from social listening to determine what is genuinely trending, notably in the realms of society, culture, the environment, and politics. The rest is pretty simple: Artificial Intelligence program DALL-E 2, which creates digital images from text descriptions, turns the hashtags or debate topics into artwork.

The open AI use policy prohibits DALL-E 2 from generating any inappropriate material.

Below are some examples of contemporary debate topics turned into art by artificial intelligence digital image generator DALL-E 2 as part of The Art of Trending project.

Recognizing that the art business is not an exception and that, like with any other sector, AI could eventually threaten artists jobs, Woord Art Institute explains that they dont view the technology as a job replacement but rather as a powerful tool for visual designers that embraces and understands.

As Ahrans tells us, the institute finds the experiment interesting not only on what AI can do but about sparking a conversation around new technologies related to art.

At the end of the day, our goal is to fuel a conversation with this experiment, Ahrans adds.

Furthermore, as tech art becomes more widely available and human art becomes more distinctive, the institute expects that AI image generators will in fact elevate the value of genuine artists work.

Interested in reading more about artificial intelligence making art? Last month, an AI-made work won a fine arts competition in the US. See here:

Art Made by AI Wins Fine Arts Competition

Editors Note:The opinions expressed here by the authors are their own, not those of Impakter.comIn the Featured Photo:Artificial intelligence generates digital image of: Iran Protests. Featured Photo Credit: Woods Art Institute.

Read more here:
'The Art of Trending': AI Creating Artwork From Topics Trending on Twitter - Impakter

The EU Artificial Intelligence Act (AI Act), proposed by the European Commission in April 2021, is an ambitious and welcome attempt to develop rules for artificial intelligence, and to mitigate its risks. The current text, however, is based on a linear view of the AI value chain, in which one entity places a given AI system on the market and is made accountable for complying with the regulation whenever the system is considered high risk. In reality, the AI value chain can present itself in a wide variety of configurations. In this paper, in view of the many limitations of the Act, we propose a typology of the AI value chain featuring seven distinct scenarios, and discuss the possible treatment of each one under the AI Act. Moreover, we consider the specific case of general-purpose AI (GPAI) models and their possible inclusion in the scope of the AI Act, and offer six policy recommendations.

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Reconciling the AI Value Chain with the EU's Artificial Intelligence Act - CEPS

Chicago, Oct. 06, 2022 (GLOBE NEWSWIRE) -- According to the new market research report by MarketsandMarkets, theArtificial Intelligence In Genomics Market is projected to reach USD 1,671 million by 2025 from USD 202 million in 2020, at a CAGR of 52.7% between 2020 and 2025. The need to control drug development and discovery costs and time, increasing public and private investments in AI in genomics, and the adoption of AI solutions in precision medicine are driving the growth of this market. However, the lack of a skilled AI workforce and ambiguous regulatory guidelines for medical software are expected to restrain the market growth during the forecast period.

Browse in-depth TOC on "Artificial Intelligence (AI) in Genomics Market"141 Tables24 Figures154 Pages

Download PDF Brochure: https://www.marketsandmarkets.com/pdfdownloadNew.asp?id=36649899

List of Key Players in Artificial Intelligence in Genomics Industry:

Drivers, Restraints, Challenges and Opportunities in Artificial Intelligence in Genomics Industry:

Key Findings of Artificial Intelligence in Genomics Market Study:

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Based on offering, the AI in genomics market is segmented into software and services. The software and services segment accounted for largest share of the global artificial intelligence in genomics market in 2019. Software is needed to generate new insights from large-scale datasets and help understand genomic variations, thus enhancing the search for disease-causing variants and reducing clinical analysis times. The benefits offered by AI in software are driving its adoption among end users.

Based on functionality, the AI in genomics market is segmented into genome sequencing, gene editing, clinical workflows, and predictive genetic testing & preventive medicine. Genome sequencing was the largest functionality segment in this market in 2019 and is estimated to grow at highest CAGR in coming years. The large share of this segment can be attributed to the use of AI solutions to identify chromosomal disorders, dysmorphic syndromes, teratogenic disorders, and single-gene disorders.

Geographical Growth Scenario:

The global AI in Genomics market is segmented into North America, Asia Pacific, Europe, Rest of the World. North America (comprising the US, and Canada) is expected to account for the largest share of the global AI in Genomics market in 2020, followed by Europe. The large share of North America can be attributed to the increasing research funding and government initiatives for promoting precision medicine in the US.

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Artificial Intelligence in Genomics Market worth $1,671 million by 2025 says MarketsandMarkets - Benzinga

For millennia, humans have imposed themselves on nature or other humans by mastering the art of melting and mixing metals: the Copper Age was followed by the Bronze or Iron Age. Modern steel is at the base of the Industrial Revolution of the late 18th and 19th centuries. In the 20th century, aluminum alloys, titanium or superalloys allowed enormous technological leaps in cars, planes, missiles, prostheses In the second decade of this millennium, a machine has discovered several alloys that equal and even surpass those created by humans in some of its properties.

A group of researchers from prestigious European technical research centers, from the Max Planck Institute for Metallurgical Research to the Delft University of Technology, passing through the Royal Institute of Technology in Stockholm, have now created a machine learning system (machine learning , in English) capable of diving among millions of combinations between the different elements of the periodic table, finding 1,000 candidates with the properties that interested them and analyzing them looking for those that theoretically would have a low coefficient of thermal expansion (the expansion or contraction of the material with cold or heat). As published in the magazine Sciencefound four new alloys with a coefficient equal to or lower than the most temperature-immune combinations used so far.

Until a few years ago, an alloy was essentially a mix between a parent metal and small concentrations of other elements from the periodic table. The rules of metallurgy almost forbade going further. The director of IMDEA Materials, Jos Manuel Honrubia, exemplifies this by comparing a coffee with an alloy based on iron. By dissolving the sugar, you get a single liquid with properties different from those of coffee and sugar separately. In alloys it is similar, but there are limits to the proportion of other elements that you can add to iron before there are precipitates that are no longer part of the main alloy and generally worsen its properties. All of this was blown up in 2004: Then two independent groups combined five elements in similar proportions, seeing that they formed a single unique solution, he says. This opened a new era in materials science, that of high-entropy alloys. But there was a new challenge: finding new combinations between a main element and smaller quantities of two or three others (steel is iron with three or four additions) was a difficult task, but feasible. Before this time, the addition of many alloying elements in large proportions was a problem. In those of high entropy, the possible new compositions of dozens of elements and their different concentrations are estimated to exceed 10. An amount impossible for humans to handle, but less so for machines.

Compared to traditional methods, machine learning is much more efficient, saving time and effort

Ziyuan Rao, scientist at the Max Planck Institute for Metallurgical Research

The researcher at the Max Planck Institute and first author of the research, Ziyuan Rao comments on the main advantage of his artificial intelligence (AI) system: Compared to traditional methods, machine learning is much more efficient, saving time and effort , He says. For most of history, the discovery of new alloys with better properties has been based on trial and error, the knowledge accumulated by craftsmen or directly serendipity. This is the case of Toledo steel, whose swords were feared for centuries. As the director of the National Center for Metallurgical Research (CENIM-CSIC) Carlos Capdevila recalls, they forged them with charcoal from nearby mountains, which contained more carbon than other swords in Europe, giving them more hardness. Materials science currently relies on computer programs and models that save calculations and anticipate results, but the decisive work remains human.

Rao and his colleagues artificial intelligence system consists of three basic steps. They first use a model that generates new mixtures from a database that the researchers had previously assembled. This is because high-entropy alloys have a huge compositional spectrum and it is almost impossible to cover all possible compositions, he details. In a second step, they use another model to predict the properties of the compositions they obtained in the first. In a final step, the system scores the candidates (in this case 1,000) by combining the expected coefficient of each with their degree of novelty.

They thus arrived at four new alloys that they compared with invar. It is an alloy that, in its original mixture, had 64% iron, another 36% nickel and small amounts of manganese, carbon and chromium. Discovered at the end of the 19th century, whose discovery earned the Nobel Prize for its creator, the Swiss Charles douard Guillaume, it had a very low coefficient of thermal expansion. Not being affected by thermal changes, it was and still is essential in the design of precision instruments, clocks, pendulums, motor valves, mechanics of telescope optics Rao assures that two of the alloys created by their intelligence system equals invar alloys and two others have the lowest coefficient of thermal expansion of high or medium entropy alloys.

Stefan Bauer, a researcher at the Royal Institute of Technology in Stockholm and one of the senior authors of this research, recalls in a note: Machine learning models have been incredibly successful when unlimited amounts of data are available, for example in video games. . However, in the real world, it is much more difficult to find use cases where artificial intelligence makes a difference. It is very exciting to see that the predictions were not only tested in simulations, but that new alloys were created and physically demonstrated. Having proven its worth with thermal expansion, the scientists intend to use their machine learning system to investigate other properties, such as magnetism, in other materials.

Jon Mikel Snchez is a researcher in advanced materials at Tecnalia. A few years ago he did his doctoral thesis on high-entropy alloys. When he is asked about the possible properties beyond thermal expansion of these alloys and their possible applications, he almost runs out of paper. There are so many alloys that have improved the traditional ones in many aspects. Some scientists compare the discovery of it with that of steels. Some have better magneto-thermal properties. Others have better cryogenic performance, key for fuel storage. He also recalls a high-entropy titanium alloy that outperforms the best titanium alloy used in prosthetics today. Lastly, one of the most important and the one that mortals understand best, better structural properties (vehicle parts, for example) especially at high temperatures. Hence, Snchez believes, the relevance of these works. Applying AI to discover new alloys is quite new. Discovering new materials by these methods is a significant advance, he says.

Capdevila, the director of CENIM, comments that discovering a new alloy or improving the properties of existing ones by slightly modifying their composition has its advantages. He gives the example of the cover that they are going to put on the Santiago Bernabu soccer field. Stainless steels have a high reflectance and without modifying them, the temperature on the surrounding terraces would be very high. However, the alloy they will put in neutralizes most of the heat. Discovering a new alloy would be for a doctoral thesis of four or five years, now the machine does it in a few days. But Capdevila emphasizes that the human part is still there. Its computing power, but I, human, tell it what parameters interest me.

Torralba, the director of IMDEA Materials, is convinced that high entropy alloys are beginning a new era. They promise improvements in highly demanded properties, such as certain magnetic properties, high resistance to corrosion, greater tolerance to extreme temperatures or thermal changes and remember that one of the obstacles to the development of fusion energy is the lack of a material that can withstand the high temperatures generated in a fusion reactor. In all technologies, progress depends on the necessary materials being available, he recalls.

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Artificial intelligence discovers the 'toledano steel' of the future - Morning Express

Brief History

The field of artificial intelligence as we know it today began in the 1940s. World War II and its need for rapid technological advancement to fight the enemy spurred on the creation of this field thanks to the likes of mathematician Alan Turing and neurologist Grey Walter. These men, and many others like them, began to exchange ideas regarding the various possibilities of intelligent machines and what would count as an intelligent machine.

It wasn't until the 1950s, however, that the actual term 'artificial intelligence' was coined by computer scientist John McCarthy. During this time, scientist Marvin Minsky's ideas on how to pre-program computers with rules of intelligence would come to dominate the coming decades. In fact, he and McCarthy received a lot of funding to develop AI in the hopes of getting an upper hand against the Soviet Union. However, Minsky's predictions about artificial intelligence (namely the pace of its advancement) fell woefully flat over time.

It was also in the late 1960s that the first mobile decision making robot capable of various actions was made. Its name was Shakey. Shakey could create a map of its surroundings prior to moving. However, Shakey was very slow in its ability to sense the surrounding environment. Shakey was a good example of the shaky ground AI was on at the time.

This is because in the 1970s, owing to a derisive and what would ultimately prove to be a wrong conclusion by mathematician Sir. James Lighthill about AI's capabilities, AI hit a snag. Funding was massively slashed for AI projects and very little development occurred during this decade.

But by the early 1980s, AI started to receive funding for commercial projects as companies noted that AI had a use for specific niches that could save them money. In the 1990s, AI had a mini-revolution of sorts. Many in the field discarded Minsky's approach to AI and, instead, adopted the approach pushed by Rodney Brooks. Instead of pre-programming a computer with algorithms of intelligence, as Minsky advised, Brooks advised that AI be built with neural networks that worked like brain cells and thus learned new behaviors. Brooks didn't come up with this idea himself but he did help bring it back to life. In fact, you can thank Brooks' company for coming up with the first widely used robot for the home, the Roomba vacuum.

Besides the Roomba vacuum, the 2000s had a lot going on in AI. Maybe you've seen Youtube clips of the robot BigDog? It looks like a big scary metallic dog-horse of some sort. It was built to function as an artificial pack animal in rough terrain for the military. Or, perhaps you've heard of PackBot? This is a bomb disposal robot that has been used in the Middle East by U.S. troops.

Even if you haven't heard of these incredible machines, then you've almost certainly heard of speech recognition on your cell-phone, speech recognition that learns your voice and becomes better over time. That's another great example of AI in the modern world.

If you're a fan of Jeopardy then you saw AI function under the name 'Watson', a machine system that beat the top two Jeopardy champions of all time in answering a wide variety of question. Watson's technology now helps give doctors recommendations about their patients.

Today's artificial intelligence hits on almost every aspect of society, from the military and entertainment to your cell phone and driverless cars, from real time voice translation to a vacuum that know where and how to clean your floor without you, from your own computer to your doctor's office.

So what where is AI going in the future? No one can tell you for sure but here are some possible ideas:

Some people claim that, no matter, what machines will never be truly intelligent. However, it's a matter of debate as to what intelligence actually is and how you can actually gauge it. So far, AI has been limited to very specific tasks and in some of those tasks it has become better than humans, such as playing chess. In more complex tasks, like speech recognition, it's not as good as you and I (at least not yet). In some limited ways, computers are already more intelligent than people. For instance, unlike people, they aren't influenced by unintelligent superstitions (unless programmed to be). The idea for whether or not a machine will ever truly surpass all of your intellectual abilities and be able to learn new things and make decisions on par or better with humans is simply unknown. Many will argue yes and no. Perhaps, there will be no actual delineation between AI and human in the future. We may simply, albeit slowly, merge into one in the future and become completely inseparable.

Artificial intelligence (AI) is the ability of a computer to perform tasks that are similar (at least in a limited sense) to that of human learning and decision making. AIs roots go back to the 1940s, with Alan Turing and Grey Walter. In the 1950s, John McCarthy coined the term 'artificial intelligence' and Marvin Minsky was a well-known scientist of the field. In the 1980s, companies began using AI to save money and in the 1990s and 2000s the field of AI really took off with the likes of Watson, speech recognition, and a lot more.

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What is Artificial Intelligence? - Definition & History | Study.com