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Marco Stefanini, Global CEO Global and founder of the Brazilian multinational, will be present in the annual event and will have an article of his in the INSEAD Global Talent Competitiveness Index Report

In the year in which it reaches its 50th anniversary, the World Economic Forum, a big annual event that reunites the main leaderships and authorities of the planet in the political and economic scenes will count on Stefaninis participation, one of the most important providers in global business solutions based on digital technologies. The event will take place from the 21st to the 24th of January 2020 in Davos in the Swiss Alps. Marco Stefanini, Global CEO and founder of the Brazilian multinational, will be present along with Felipe Monteiro, Strategy professor at INSEAD and Director of The Global Talent Competitiveness Index (GTCI).

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During the annual event, which will have as a central theme Stakeholders for a more cohesive and sustainable world, the INSEAD 2020 GTCI Report will be launched on January 22nd at the Sustainable Development Goals (SDG) Tent. The report will showcase an article titled Latin America: The next source of talent in AI? written by Marco Stefanini in partnership with Fbio Caversan, Artificial Intelligence Research & Development Director of Stefanini USA.

On Chapter 2 of the important global report, the Brazilian multinational evaluates the scope of the Science of Artificial Intelligence and technology in Latin America. Additionally, it highlights Marco Stefaninis vision for the current and future scenarios of this theme, which has been the keynote of the disseminated digital transformation.

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For several years, Stefanini has been maintaining a solid partnership with INSEAD, one of the worlds largest and most prestigious business schools and will promote in 2020 the 3rd class in the Leadership Transformation Program, which will take place from March 28th to April 4th on INSEADs Fontainebleau campus in France. The Leadership Transformation proposes a journey of discoveries and knowledge so that high leaderships can surpass limits through collaboration and innovation amongst each other.

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Stefanini Participates in the 2020 Davos World Economic Forum and Brings Its Experience in Artificial Intelligence - MarTech Series

New Delhi, January 7, 2020 International Data Corporation (IDC) has revealed predictions impacting the Artificial Intelligence (AI) investments for organizations in India for 2020 and beyond. In this data-driven era, AI is becoming a priority for organizations in India, driven by the need to automate, faster route to market, and agility. Organizations in India are warming up to leveraging AI when it comes to automating processes, deriving actionable insights, and prototyping with use cases to evaluate the implication of the technology.

According to Rishu Sharma, Principal Analyst, Cloud and Artificial Intelligence, IDC India, "Indian organizations are looking at leveraging AI driven by the need for automation to increase the productivity. As we deal with large set of unstructured data being created in the digital era, AI will be the backbone when it comes to extracting valuable insights. Unsurprisingly, the primary reason holding the organizations back when it comes to implementing AI technology is - the trustworthiness of the data. Enterprises also cite unrealistic expectations, lack of skilled staff and unclear business case, as the reasons for failure of AI projects."

IDC India has listed the following ten predictions that would impact the technology buyers and suppliers in AI in India in 2020 and beyond:

#1 Outcomes as a Service: By 2025, AI will be integral to every part of the business in India, resulting in 20% of the overall spend on AI solutions as "outcomes as a service" that drives innovation at scale and superior business value.

#2 Worker Augmentation: By 2024, 50% of enterprises in India will invest in employee retraining and development, including third-party services, to address new skill needs and ways of working resulting from AI adoption.

#3 Digital Trust: By 2024, top 100 organizations in India will have formal programs to monitor their "digital trustworthiness" as digital trust becomes a critical corporate asset.

#4 Intelligent Process Automation: By 2024, 50% of enterprises in India will embed intelligent automation into technology and process development, using AI-based software to discover operational and experiential insights to guide innovation.

#5 AI is the New UI: By 2024, AI in India will become the new user interface by redefining user experiences where over 20% of user touches will be augmented by computer vision, speech, natural language, and AR/VR.

#6 Hyper-Personalization: By 2023, 10% of customer experience applications in India will be continuously hyper-personalized by combining a variety of data and newer reinforcement learning algorithms.

#7 AI Edge Applications: By 2025, 30% of computer vision and speech recognition models in India will run on the edge (including endpoints) and feature deep learning on convolutional and recurrent neural networks.

#8 Embedded AI: By 2025, at least 50% of new enterprise application releases in India will include embedded AI functionality, but truly disruptive AI-led applications will represent only about 5% of this total.

#9 AI Accelerated Chips: In 2023, technology buyer spending on semiconductors (GPUs, FPGAs, AI ASICs, and AI ASSPs) used specifically to accelerate AI training and inferencing will reach nearly $120 million in India.

#10 AI Edge Computing: By 2024, nearly 10% of servers that process AI workloads using AI-optimized processors and coprocessors in India will be deployed at the edge.

"Artificial Intelligence in India promises to develop advanced solutions that tackle organizational challenges and speed up strategic formulation. With the increasing availability of data, AI can be effective in multiple applications across various verticals including agriculture, transportation, finance, healthcare, retail and many more. India, being the key contributor in the global startup ecosystem, will play a crucial role in discovering the benefits of AI technology across varied sectors," says Swapnil Shende, Senior Market Analyst for AI at IDC India.

These strategic predictions for the India market are presented in full in the following report: IDC FutureScape: Worldwide Artificial Intelligence 2020 Predictions India Implications (IDC #AP45727419 ) and is part of India Artificial Intelligence and Cloud Services report series

IDC FutureScape reports are used to shape IT strategy and planning for the enterprise by providing a basic framework for evaluating IT initiatives in terms of their value to business strategy now and in the foreseeable future. IDC's FutureScapes are comprised of a set of decision imperatives designed to identify a range of pending issues that CIOs and senior technology professionals will confront within the typical three- to five-year business planning cycle.

International Data Corporation (IDC) is the premier global provider of market intelligence, advisory services, and events for the information technology, telecommunications, and consumer technology markets. With more than 1,100 analysts worldwide, IDC offers global, regional, and local expertise on technology and industry opportunities and trends in over 110 countries. IDC's analysis and insight helps IT professionals, business executives, and the investment community to make fact-based technology decisions and to achieve their key business objectives. Founded in 1964, IDC is a wholly-owned subsidiary of International Data Group (IDG), the world's leading tech media, data and marketing services company. To learn more about IDC, please visit http://www.idc.com. Follow IDC on Twitter at @IDC and LinkedIn. Subscribe to the IDC Blog for industry news and insights: http://bit.ly/IDCBlog_Subscribe.

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IDC unveils Top 10 Artificial Intelligence predictions 2020 and beyond for the Indian market - TelecomTV

(Photo : silvawpius.wordpress.com)What are the causes of racial discrimination in artificial intelligence in cars? How does it happen and can it be avoided at all. Does AI really abstract or it is just a set of algorithms too.

When the first singularity called the "big bang" seeded the proto-universe with light and matter that was the first proto-matter into the universe today. What made the universe into what is it now, is the mysterious substance called "Dark matter". In the first few seconds of the big bang, it was so hot, when it cooled down dark matter settled. Gravity and the fundamental forces of the universe pulled all dark matter from heated halos that became everything in the universe.

Now, this dark matter is captured as visual imaged or as background radiation in the galaxy, we know today. Dark matter holds everything in the cosmos together, without it, there is no telling what can happen. Here are insights into what kinds of dark matter that the big bang cooked up, basically everything in the universe floats in a sea of endless dark matter. Kinds of dark matter as defined are warm, cold, and fuzzy, the reason is the scientist give these terms is to make them understandable. Most of the time, everyone gets lost in the play of concepts and terms. Let us begin now.

Factoid#1

Specialists from MIT, Princeton University, and Cambridge University have speculated that the proto-galaxies to later galaxies are not the same. This is because of whether it was a warm, cold, or fuzzy matter when they were formed. A simulation was designed to test the theory on dark matter formations.

Factoid#2

Most dark matter iscoldand does not mix with other matters.Warmis lighter and moves fast, not slow, a bit faster than cold DM. A new concept isfuzzydark matter which is ultralight bits and particles that heavier than an electron. Fuzzy dark matter is essentially heavier, and larger too.

Factoid#3

Most dark matter used to form halos around proto-galaxies yet to form were cold. If it was the fuzzy or warm kind, then galaxies will have trailing tails. Fuzzy universes might look striated, like harp strings.

Factoid#4

Light traveling in the cosmos can be very old, using a telescope that will tell if the dark matter is cold, warm, or fuzzy too. These three kinds of dark matter (DM) is about 85% in the universe today.

Factoid#5

Proving what dark matter is harder to do, and most guesses point at dark matter are cold mostly. And, this is what makes the superstructure of the universe and keeps it together like crazy glue,

Factoid#6

Fuzzy dark matter is totally different, and it acts like a wave throughout the universe. This wave-like dark matter is like to mix with other bits of matter, compared to cold dark matter. Galaxies formed from it will be significantly different from what it is now.

Factoid#7

The scientist is developinga new universal modelof what a fuzzy matter universe will be like. Using the James Webb Space Telescope, they will look back in time and see the first proto-galaxies as they were. Hopefully models by Mocz, Fialkov, Vogelsberger will be proven by then.

Related Article: Is Dark Matter Warm, Cold, or 'Fuzzy'? New Simulations Provide Intriguing Insights.

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Should Artificial Intelligence in Cars Be Programmed to Be Racism-Free? - Science Times

Disruption is widespread in almost any sector as technology like artificial intelligence (AI) is making its way into core businesses to improve processes, including airline operations. In akeynote speechat the annual Consumer Electronics Show, Delta Air Lines CEO Ed Bastian used the forum to discuss the operational structure for Delta, which will be driven by an AI machine learning tool.

Per an Avionics International report, Bastian did not provide a specific product name for the technology, but instead called it a proprietary tool that will mainly be focused on helping passengers and flight crews overcome weather occurrences that impact the routes they fly on a daily basis. The keynote speech is a familiar strategy across all of the divisions of Delta, including their maintenance team whose predictive maintenance leadership gave a speech on how the airline is shifting towards the adoption of AI at the 2019AEEC/AMC annual conference.

Broadly speaking, the AI tool will help improve airline operations in the midst of extreme weather conditions.

Weve cancelled cancellations, but we still have to deal with weather variables like hurricanes or a nasty Noreaster, and thats why the team in our operations and customer center is developing the industrys first machine learning platform to help ensure a smooth operation even in extreme conditions. The system uses operational data to run scenarios and project future outcomes while simulating all the variables of running a global airline with more than 1,000 planes in the sky, Bastian said.

Airline industry innovation can also benefit the US Global Jets ETF (NYSEArca: JETS). JETS seeks to track the performance of the U.S. Global Jets Index, which is composed of the exchange-listed common stock or depository receipts) of U.S. and international passenger airlines, aircraft manufacturers, airports, and terminal services copanies across the globe.

U.S. airlines are headed for a 10thstraight year of profits, which is causing employees to demand higher wages as well as increased benefits. This decade of profitability could put airlines-focused and transportation ETFs in play.

Next year, major U.S. carriers will be negotiating labor agreements with more than 120,000 unionized employees, a process that is set to add to their expenses, aCNBC article noted. American will be negotiating with most of its unionized workforce, including pilots, flight attendants, and maintenance workers.

Labor costs are airlines biggest expense and they have become a larger portion of overall costs, the report added. Last year, labor costs ate up 28% of U.S. airlines $187 billion in revenue, up from a 21% share in 2008, as airlines hired more workers and compensation rose,according to data from trade group Airlines for America.

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Delta Air Develops A.I. Tool to Address Weather Disruptions - ETF Trends

Artificial intelligence (AI), the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings. The term is frequently applied to the project of developing systems endowed with the intellectual processes characteristic of humans, such as the ability to reason, discover meaning, generalize, or learn from past experience. Since the development of the digital computer in the 1940s, it has been demonstrated that computers can be programmed to carry out very complex tasksas, for example, discovering proofs for mathematical theorems or playing chesswith great proficiency. Still, despite continuing advances in computer processing speed and memory capacity, there are as yet no programs that can match human flexibility over wider domains or in tasks requiring much everyday knowledge. On the other hand, some programs have attained the performance levels of human experts and professionals in performing certain specific tasks, so that artificial intelligence in this limited sense is found in applications as diverse as medical diagnosis, computer search engines, and voice or handwriting recognition.

All but the simplest human behaviour is ascribed to intelligence, while even the most complicated insect behaviour is never taken as an indication of intelligence. What is the difference? Consider the behaviour of the digger wasp, Sphex ichneumoneus. When the female wasp returns to her burrow with food, she first deposits it on the threshold, checks for intruders inside her burrow, and only then, if the coast is clear, carries her food inside. The real nature of the wasps instinctual behaviour is revealed if the food is moved a few inches away from the entrance to her burrow while she is inside: on emerging, she will repeat the whole procedure as often as the food is displaced. Intelligenceconspicuously absent in the case of Sphexmust include the ability to adapt to new circumstances.

Psychologists generally do not characterize human intelligence by just one trait but by the combination of many diverse abilities. Research in AI has focused chiefly on the following components of intelligence: learning, reasoning, problem solving, perception, and using language.

There are a number of different forms of learning as applied to artificial intelligence. The simplest is learning by trial and error. For example, a simple computer program for solving mate-in-one chess problems might try moves at random until mate is found. The program might then store the solution with the position so that the next time the computer encountered the same position it would recall the solution. This simple memorizing of individual items and proceduresknown as rote learningis relatively easy to implement on a computer. More challenging is the problem of implementing what is called generalization. Generalization involves applying past experience to analogous new situations. For example, a program that learns the past tense of regular English verbs by rote will not be able to produce the past tense of a word such as jump unless it previously had been presented with jumped, whereas a program that is able to generalize can learn the add ed rule and so form the past tense of jump based on experience with similar verbs.

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artificial intelligence | Definition, Examples, and ...