Category Archives: Artificial Intelligence

How smart is it to trade artificial intelligence stocks?

Artificial intelligence (AI) is becoming more and more a part of our lives

The U.S. economy is evolving at an unprecedented pace. Companies that fail to innovate disappear, and companies like those in the FANG group step in to replace them.

Unlike the tech stocks from the dot-com bubble, these companies have plenty of room to expand and innovate. Several of them are investing in AI.

AI doesnt have the best reputation pop culture often portrays it as the enemy. Let me assure you In its current form, artificial intelligence cant take over the world.

But, yeah, these companies can make for smart trades if you do your research. Heres what to watch in 2021

Artificial intelligence stocks had a great year in 2020. Leading AI companies like Apple (NASDAQ: APPL), NVIDIA (NASDAQ: NVDA), and Facebook (NASDAQ: FB) all had massive gains in the hot 2020 market.

A lot of companies, big and small, have been integrating software into their businesses. Automation is huge. And intelligent analysis of consumer data helps companies be more efficient.

These productive technologies are made possible by advancements in artificial intelligence.

And as AI technology advances, artificial intelligence products will become less expensive. That means more companies could use the technology.

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I think were only seeing the beginning of a massive AI revolution. As AI technology improves, more companies will start using it on a massive scale.

In 2021, it could become an even bigger part of our everyday lives. Its likely that more companies will adopt AI-powered tools and technology. And that can mean even more growth in this industry. The impressive gains and growth of many tech stocks can reflect that.

Ill be the first to warn you: AI stocks can be risky. They dont move slowly like blue-chip stocks.

If were talking about artificial intelligence penny stocks, the risks are even higher. But I love to take advantage of penny stock volatility. And with the right risk management strategy, artificial intelligence penny stocks could provide great trading opportunities.

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Artificial intelligence companies arent all the same. They tend to work on different versions of AI and target different sectors.

Picking AI stocks can be difficult few publicly traded companies solely focus on AI. The industry is still in its early stages, but as it cements itself into the economy, more companies will become publicly tradable.

Here are a few artificial intelligence stocks to watch in 2021 Note that Im watching these stocks. Theres no guarantee any of them will offer a trading opportunity.

For more tips on how to build and maintain a watchlist, check out this post.

Want to know which stocks Im watching each week? Subscribe to my no-cost weekly watchlist. Ill send my picks right to your inbox each week.

NVDA is my favorite AI stock for 2021. Its currently the leading supplier of graphics cards for computer gaming and AI systems. Its chips specialize in the deep-learning category of AI. Thats key to the entire artificial intelligence process.

Other companies have used NVDA chips to analyze large sets of data to train their models. These systems analyze old data to find patterns that can be applied to new data as it enters the system. (Kinda like smart traders do with the markets.)

Overall, NVDA is a well-positioned company. Many of the companies below use NVDA graphics cards.

CRM is an impressive growth story on its own, but its venture into AI could position the company for steady growth for years to come.

CRM developed its Einstein tools to improve companies sales forecasts. Einstein analyzes a companys historical account data to predict which deals are most likely to close. If a company knows one deal has a better chance of closing than another, it can better allocate its resources.

MSFT has dominated the business world since its inception. It was one of the few tech stocks to survive the dot-com crash, and it continues to expand.

In 2017, MSFT acquired Canadian AI company Maluuba. Then in 2018, it acquired two more AI companies. Given MSFTs domination of the computer market, the companys entrance into this industry shouldnt be taken lightly.

Like Microsoft, Alphabet (Googles parent company) started its AI endeavors by acquiring smaller, nonpublic AI companies. And so far, its acquired more of these companies than any other tech firm.

Just imagine how AI could improve search engines. And GOOG owns several companies in the automated driving space that could benefit from breakthroughs in artificial intelligence.

AAPL is the largest company in the world by market capitalization. The companys focus on AI has been primarily for its consumer devices the goal is to improve the user experience. Most of its new devices have special chips to power the complicated AI engine.

To date, the company has sold over one billion iPhones That means AAPL has the potential to put AI into the hands of over one billion people. That can have a worldwide impact.

FB has faced multiple scandals. Even so, the stock is near all-time highs. The companys a cash-flow machine, and there are no signs that its ad revenue will slow down.

Adding AI to FBs algorithm could increase profitability. It could help to better target ads and entice companies to spend more money on advertisements.

BIDU is called the Google of China. The companies have similar business models and help consumers navigate the internet. Like Alphabet, BIDU is heavily investing in AI because of the technologys ability to improve its search engine.

China has a more competitive search engine market than the U.S., and Baidu believes AI can give its product a competitive advantage.

CLDR is a lower-priced AI stock to watch.

It provides data analytics and management products to countries all over the world. Its signature product, Cloudera Enterprise Data Hub, allows companies to run analytical queries on private cloud data centers. CLDRs tools use AI to enhance its analytics.

Its good to see whats going on at the upper end of the market. Thats where the big money is. But that doesnt mean there arent smaller companies looking to leverage AI in their operations.

As artificial intelligence continues to grow and more corporations use it in their daily operations, more companies will flood into the industry. Understanding the big picture can help you be a smarter trader.

These companies are less established than the ones listed above, but they have explosive growth potential if this industry takes off.

Remember: Always do your own research before making any trades. Make a detailed trading plan and stick to it. Never risk more than you can afford. Always be careful when buying any stock online and remember to cut losses quickly.

EGAN is a U.S.-based software-as-a-service company. It provides cloud software to automate customer engagement using AI-powered analytics and machine learning.

EGANs products allow companies to analyze consumer data. The stock had an impressive run in October 2020, but its back to trading around $10.

DUOT develops AI technology platforms and applications. Its main application is the automated inspection of rail cars while theyre in motion. The company also offers homeland security applications to secure bridges and tunnels.

OK, enough with the boring large-cap and high-priced AI stocks Lets look at some artificial intelligence penny stocks that could have the kind of potential I look for in trading.

CMCM is in the competitive telecommunication industry thats dominated by Verizon (NYSE: VZ) and AT&T (NYSE: T).

The Cheetah Keyboard uses AI to personalize the smartphone experience.

LAIX is a Chinese AI stock. It offers online educational products and services. Its platform includes an AI teacher that interacts with students as they learn English.

The stock went supernova in February. Former runners have the potential to spike again with a news catalyst and high volume.

AITX is the lowest-priced AI stock to watch and one of my recent favorites. The company offers AI and robotic solutions for the security and monitoring industries.

The company recently announced new deals and work contracts, which drove up the price. I traded this stock multiple times in 2020 and 2021 for total profits of $32,152.55 as of this writing.*

(*Please note: My results are far from typical. Individual results will vary. Most traders lose money. I have the benefit of years of hard work, dedication, and experience. Trading is inherently risky. Do your due diligence and never risk more than you can afford to lose.)

I only trade stocks listed on U.S. exchanges. But here are a few artificial intelligence stocks that do business worldwide

INOD is a tech engineering company that operates worldwide. It offers solutions to data challenges that can come up when companies try to implement AI technology.

The stock has been trending higher since mid-2020.

MARK is a U.S.-based company that does business worldwide. It develops AI-based tech products and solutions for businesses.

This AI stocks a former multiday runner. Thats something I always look for in the penny stocks I like to trade.

KXSCF is a Canadian company that uses AI to help supply chain businesses plan for the future, monitor risk, and make decisions.

The stock trades on the OTC markets and has very little volume.

BB is a Canadian AI stock. The company uses AI for cybersecurity solutions.

The stock was recently targeted by Reddit meme stock traders. The chat room hype caused the stock and many others to go supernova as part of giant short squeezes.

As a trader, you should always be prepared to take advantage of opportunities. As AI technology advances and new uses for AI are discovered, there could be plenty of trading opportunities in artificial intelligence penny stocks.

Were already seeing a lot of it in self-driving cars. Who knows what future invention will change our lives?

But AI stocks are like any other sector. They can be hot or cold. And news can come and go.

Learn to take advantage of the hype, news, and volume in AI stocks. Build a watchlist first, then pay attention to the news and watch for your pattern or strategy.

Artificial intelligence stocks are stocks of companies involved in the development of computer programs that perform tasks that would usually require human intelligence think voice recognition, self-driving cars, etc.

The best artificial intelligence stocks for trading are the ones that fit your style and strategy. If you like to invest in AI stocks, you might prefer large-cap stocks. If you want to trade artificial intelligence penny stocks, keep a watchlist to see whats moving. Do your research! And if you need a trading mentor, apply for my Trading Challenge.

All the major auto manufacturers are racing to develop artificial intelligence for self-driving cars. But theyll likely partner with companies like Aptiv (NYSE: APTV) and Intel (NASDAQ: INTC), which are already developing automotive smart chips and sensors.

That depends on the stock, your strategy, and the news. AI technology is still in its infancy. Still, theres a lot of potential in this sector. AI stocks are worth following and monitoring on your watchlist for potential trades.

I dont invest in stocks I trade them. And trading AI stocks works the same as it does in other sectors: filtering down stocks, creating a watchlist, and having a trading plan ready for if and when they meet your personal trading criteria.

The AI revolution is off to a great start But the technologys just beginning to take off.

The unlimited potential of AI could have a huge impact on how we live in the future. And 2021 could be a big year for this sector.

Do you know how to prepare for a possible artificial intelligence penny stock run?

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What do YOU think of these AI stocks to watch in 2021? Leave a comment and let me know!

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The Top 17 Artificial Intelligence Stocks ... - Timothy Sykes

Is robotics part of AI? Is AI part of robotics? What is the difference between the two terms? We answer this fundamental question.

Robotics and artificial intelligence (AI) serve very different purposes. However, people often get them mixed up.

A lot of people wonder if robotics is a subset of artificial intelligence. Others wonder if they are the same thing.

Since the first version of this article, which we published back in 2017, the question has gotten even more confusing. The rise in the use of the word "robot" in recent years to mean any sort of automation has cast even more doubt on how robotics and AI fit together (more on this at the end of the article).

It's time to put things straight once and for all.

The first thing to clarify is that robotics and artificial intelligence are not the same things at all. In fact, the two fields are almost entirely separate.

A Venn diagram of the two fields would look like this:

As you can see, there is one area small where the two fields overlap: Artificially Intelligent Robots. It is within this overlap that people sometimes confuse the two concepts.

To understand how these three terms relate to each other, let's look at each of them individually.

Robotics is a branch of technology that deals with physical robots. Robots are programmable machines that are usually able to carry out a series of actions autonomously, or semi-autonomously.

In my opinion, there are three important factors which constitute a robot:

I say that robots are "usually" autonomous because some robots aren't. Telerobots, for example, are entirely controlled by a human operator but telerobotics is still classed as a branch of robotics. This is one example where the definition of robotics is not very clear.

It is surprisingly difficult to get experts to agree on exactly what constitutes a "robot." Some people say that a robot must be able to "think" and make decisions. However, there is no standard definition of "robot thinking." Requiring a robot to "think" suggests that it has some level of artificial intelligence but the many non-intelligent robots that exist show that thinking cannot be a requirement for a robot.

However you choose to define a robot, robotics involves designing, building and programming physical robots which are able to interact with the physical world. Only a small part of robotics involves artificial intelligence.

A simple collaborative robot (cobot) is a perfect example of a non-intelligent robot.

For example, you can easily program a cobot to pick up an object and place it elsewhere. The cobot will then continue to pick and place objects in exactly the same way until you turn it off. This is an autonomous function because the robot does not require any human input after it has been programmed. The task does not require any intelligence because the cobot will never change what it is doing.

Most industrial robots are non-intelligent.

Artificial intelligence (AI) is a branch of computer science. It involves developing computer programs to complete tasks that would otherwise require human intelligence. AI algorithms can tackle learning, perception, problem-solving, language-understanding and/or logical reasoning.

AI is used in many ways within the modern world. For example, AI algorithms are used in Google searches, Amazon's recommendation engine, and GPS route finders. Most AI programs are not used to control robots.

Even when AI is used to control robots, the AI algorithms are only part of the larger robotic system, which also includes sensors, actuators, and non-AI programming.

Often but not always AI involves some level of machine learning, where an algorithm is "trained" to respond to a particular input in a certain way by using known inputs and outputs. We discuss machine learning in our article Robot Vision vs Computer Vision: What's the Difference?

The key aspect that differentiates AI from more conventional programming is the word "intelligence." Non-AI programs simply carry out a defined sequence of instructions. AI programs mimic some level of human intelligence.

One of the most common examples of pure AI can be found in games. The classic example of this is chess, where the AI Deep Blue beat world champion, Gary Kasparov, in 1997.

A more recent example is AlphaGo, an AI which beat Lee Sedol the world champion Go player, in 2016. There were no robotic elements to AlphaGo. The playing pieces were moved by a human who watched the robot's moves on a screen.

Artificially intelligent robots are the bridge between robotics and AI. These are robots that are controlled by AI programs.

Most robots are not artificially intelligent. Up until quite recently, all industrial robots could only be programmed to carry out a repetitive series of movements which, as we have discussed, do not require artificial intelligence. However, non-intelligent robots are quite limited in their functionality.

AI algorithms are necessary when you want to allow the robot to perform more complex tasks.

A warehousing robot might use a path-finding algorithm to navigate around the warehouse. A drone might use autonomous navigation to return home when it is about to run out of battery. A self-driving car might use a combination of AI algorithms to detect and avoid potential hazards on the road. These are all examples of artificially intelligent robots.

You could extend the capabilities of a collaborative robot by using AI.

Imagine you wanted to add a camera to your cobot. Robot vision comes under the category of "perception" and usually requires AI algorithms.

Say that you wanted the cobot to detect the object it was picking up and place it in a different location depending on the type of object. This would involve training a specialized vision program to recognize the different types of objects. One way to do this is by using an AI algorithm called Template Matching, which we discuss in our article How Template Matching Works in Robot Vision.

In general, most artificially intelligent robots only use AI in one particular aspect of their operation. In our example, AI is only used in object detection. The robot's movements are not really controlled by AI (though the output of the object detector does influence its movements).

As you can see, robotics and artificial intelligence are really two separate things.

Robotics involves building robots physical whereas AI involves programming intelligence.

However, there is one area where everything has got rather confusing since I first wrote this article: software robots.

The term "software robot" refers to a type of computer program which autonomously operates to complete a virtual task. Examples include:

Software bots are not physical robots they only exist within a computer. Therefore, they are not real robots.

Some advanced software robots may even include AI algorithms. However, software robots are not part of robotics.

Hopefully, this has clarified everything for you. But, if you have any questions at all please ask them in the comments.

Do you have any fundamental robotics questions you would like answered? Tell us in the comments below or join the discussion on LinkedIn, Twitter, Facebook or the DoF professional robotics community.

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What's the Difference Between Robotics and Artificial ...

Tech expert Jarno Duursma sees both advantages and disadvantages when it comes to using AI in healthcare. First the advantages: Scientists at Life Lines, a large-scale study into the onset of chronic diseases among 165 thousand people in the northern Netherlands, make use of artificially intelligent software. Duursma: This research has been going on since 2006. A huge database is being compiled from all those studies and questionnaires. With the help of AI, doctors are able to identify connections that they would otherwise never have spotted, like improving the diagnosis of depression or the prediction of cancer.

Or what about research into medicines? At Leiden University in the Netherlands, researchers are working on a model that is based on 3.8 million measurements that have been published on drug candidates since the 1970s. This acts as a kind of library that helps scientists search in the right direction. The system also predicts interactions between a chemical and a protein based on 5.5 billion data points. Using the softwares predictions, a chemist can get to work testing whether the potential drug will work in actual practice. In this regard, the use of artificial intelligence saves a lot of time and money. These are very fine applications that allow you to develop a drug that works faster or to use an existing drug for other diseases. These are great developments that get me fired up, Duursma adds.

Something else that gets Duursma enthused: Avatars in healthcare. For example, in the form of a digital doctor who conducts a simple intake or summarizes complicated and lengthy pieces of text in a short video for patients. By letting artificial intelligence carry out an intake, a doctor has more time to spare. You can also use this digital doctor to explain patient leaflets using a video, which sometimes works better than long texts.

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Despite his enthusiasm, Duursma also warns against using AI in healthcare. Our healthcare is becoming more and more expensive and is putting more pressure on society. We need to do something about this, but we shouldnt be focusing solely on technology. We still need to keep a critical eye on the dangers of AI.

In his view, we tend to overestimate the merits of software. To illustrate this point, he points to an algorithm that predicts whether a mole is malignant or not. The software was perfectly capable of picking out bad birthmarks, but what transpired when the scientists started probing into how it did that? The system did not reach its conclusions by looking at the moles themselves, but saw the ruler that dermatologists use to track the growth of suspicious moles as an important signal. This shows that an algorithm trained with different pictures of birthmarks comes up with an assessment based on something completely different than what you might expect.

Duursma sees the same thing in a host of initiatives that were designed to detect Covid-19 on lung photos with the use of AI. These lung photos are all different qualities and there are a lot of nuances in them. So, in any event, the data is very messy. A specific AI system once again drew a conclusion on the basis of something weird. The algorithm based its diagnosis on a font on the x-ray images of certain hospitals where there were a lot of corona patients. This black box is one danger that AI poses that we need to be aware of.

According to Duursma, another disadvantage of using artificial intelligence is that we want to capture all problems as data. By this datafication of the problem, you might be needlessly diminishing the problem. This creates a techno-solutionism, whereby you only focus on where data can be collected. Whereas when you zoom out, not everything can be captured as data. These problems are then excluded from it.

Nor should we be blind, Duursma believes, to any unintended long-term consequences that technology or artificial intelligence may cause. As an example, he cites the selfie cameras in iPhones: The selfie camera has contributed to making the individual even more of a focal point. Young people now visit a plastic surgeon with their favorite Snapchat filter: This is how I want to look. Thats an unintended consequence of this technology, but no Apple developer had ever considered that before.

Duursma goes on to say that we need to pay more attention to the talents and qualities that we lose along the way as a result of technology, especially in healthcare. I used to be very good at remembering phone numbers. Now my phone does that for me. The same goes for navigating or doing math in your head. These are skills that we are losing through the use of technology. Especially in healthcare, it is important that we treat this very carefully. Look at this from the perspective of a moral compass. Imagine that we will soon have an infallible algorithm for checking moles. Are radiologists then allowed to unlearn this skill? Or do we teach students not to look at photos because the software does that? I dont have answers to these questions, but we should continue to critically examine this aspect.

Tech philosopher at Fontys University of Applied Sciences, Rens van der Vorst, also offers much the same critical examples when talking about AI in healthcare. Generally speaking, you see that the diagnostic results of algorithms are quite disappointing. Following the outbreak of corona, all sorts of claims were made. For example, about an algorithm that could predict whether someone had corona based on the sound of someones cough. All those initiatives turned out not to be so successful after all. We tend to overestimate the impact of technology in the short term but underestimate it in the long term. Maybe the same kind of thing is happening with AI.

Van der Vorst sees mainly advantages to the use of AI in logistics operations in hospitals. Technology often serves as an amplifier. So if you start using AI to help a supermarket operate more efficiently, a supermarket will operate more efficiently. The same is true for a hospital. Weve seen that software is not yet good enough at making diagnoses, but artificial intelligence is capable of planning more efficiently. AI can also play a role in preventive care right now. With measurements taken in the home and advice on healthy living, to name a few things.

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Artificial intelligence in healthcare? 'Don't focus solely on technology' - Innovation Origins

This past Thursday I held a public lecture, together with my long-time friend Ivan Bianchi, on the topic of Art and Artificial Intelligence. The event was organized by the "Galileo Festival" in Padova, for the Week of Innovation.Ivan is a professor of Contemporary Art at the University of Padova. We have known each other since we were two year olds, as our mothers were friends. We took very different career paths but we both ended up in academic and research jobs in Padova, and we have been able to take part together in several events where art and science are at the focus. Giving a lecture together is twice as fun!

The event took place in the historic "Sala Rossini" of Caff Pedrocchi (see above), in the town center, and was streamed live for online participants. We were a bit surprised to see that the hall was full of attendees, but in retrospect I think the venue, the timing, and the general organization were all playing their part to maximize the attention that the event received.Given that people are usually more interested in Art than in scientific topics I left to Ivan the better part of the hour we had, and took upon myself the task of introducing the topic, and to walk the audience through a discussion of what really is it that we talk about when we discuss Artificial Intelligence. I helped myself a bit with some material I had used earlier this year when I was invited at the Accademia dei Lincei (by its vice-president Giorgio Parisi, who a week ago won the Nobel prize in Physics!) - I will not repeat a summary of the discussion here as I did it in this other post already(which, amazingly, has already collected over 134000 page views...)

At the end of my half hour, in order to throw a bridge to the following discussion centered on art, I showed and discussed a video which showed how deep learning techniques are used to complete unfinished symphonies and works by classical music giants (Beethoven, Mahler, Schubert) - you can find the relevant material and a video at this link.

Ivan discussed how artificial intelligence is used in contemporary art nowadays. He touched on how artificial intelligence-powered instruments can be used as artistic objects (the shown case was a robotic arm which took the center stage of the Biennale 2019 in Venice) creating a performance of which they are the authors, or as support tools to produce artwork (such as robots that can sculpt marble figures and leave the artist only the final touch), or as the true subjects of the artistic production, such as a robot that creates paintings with acrylic paint on canvas. I will not go into the details of his explanation of the various trends and ideas, but you can certainly listen to the lecture in the linked video below (however, it is in Italian, unfortunately):

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Tommaso Dorigo (see hispersonal web page here) is an experimental particle physicist who works for theINFNand the University of Padova, and collaborates with theCMS experimentat the CERN LHC. He coordinates theMODE Collaboration, a group of physicists and computer scientists from eight institutions in Europe and the US who aim to enable end-to-end optimization of detector design with differentiable programming. Dorigo is an editor of the journalsReviews in PhysicsandPhysics Open. In 2016 Dorigo published the book "Anomaly! Collider Physics and the Quest for New Phenomena at Fermilab", an insider view of the sociology of big particle physics experiments. You canget a copy of the book on Amazon, or contact him to get a free pdf copy if you have limited financial means.

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Art And Artificial Intelligence: An Odd Couple? - Science 2.0

Brent Leland, founder and president of High G, will present Artificial Intelligence Fear or Opportunity Thursday, Oct. 21.

The program is being offered by the Professional Womens Connection Ripon/Green Lake chapter. Networking will begin at 5:30 p.m. and will be followed by dinner and presentation at 6.

The event will take place in the upstairs banquet area of Roadhouse Pizza, 102 Watson St.

What is artificial intelligience? The dictionary defines it as the theory and development of computer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages.

Learn how when combined with other emerging technologies, AI can deliver innovative solutions that transform businesses, disrupt markets and leapfrog the competition.

Leland will introduce the topic and begin to answer the questions that many companies are starting to ask: What is all the hype around AI? Is it relevant yet? What are the fundamentals we need to understand? How can we leverage AI with other disruptive technologies (IoT, Automation, AR/VR, etc.) to create new business models or to optimize our internal processes and capabilities? Where do we start?

Leland is the founder of High G, a boutique consulting firm focused on innovative and technology-enabled growth strategies and chaired the advisory board of Advancing AI Wisconsin.

Prior to his consulting career, Leland was the CIO of Trek Bicycle and earlier in his career held various finance, supply chain, engineering and IT roles for Spectrum Brands (formerly Rayovac), Hewlett-Packard, Loral and General Dynamics.

He holds an master of business arts degree from Stanford and a bachelor of science degree in aerospace engineering from the University of Florida.

Hes also an avid home-brewer and serves on the advisory board of Insight Brewing in Minneapolis.

Reservations must be made by Tuesday, Oct. 19 at noon and may be done by registering at https://pwcwi.clubexpress.com.

The dinner will consist of a soup, salad and assorted sandwich buffet with a cash bar.

Dietary requests should be sent to cbornick@vizance.com.

Member price is $15, while non-member cost is $20. Payment may be made online or upon arrival. Reservations made, but not honored, will be invoiced the cost of dinner selection.

Professional Womens Connection is a networking group that provides educational opportunities for area business and professional women, focusing on professional growth, personal development and the enhancement of leadership skills.

It is not a fundraising organization. The money for the annual scholarship comes from member dues, enabling current members to give back to the next generation of professional women.

Those interested in joining Professional Womens Connection may attend as a guest prior to joining the organization. Applications to join Professional Womens Connection are available through membership chair Cassie Bornick at pwc.ripon.greenlake@gmail.com and also will be available at the meeting.

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Artificial intelligence is the topic of Oct. 21 Professional Women's Connection program - Ripon Commonwealth Press

Artificial Intelligence (AI) has become a major focus of, and the most valuable asset in, many technology transactions and the competition for top AI companies has never been hotter. According to CB Insights, there have been over 1,000 AI acquisitions since 2010. The COVID pandemic interrupted this trajectory, causing acquisitions to fall from 242 in 2019 to 159 in 2020. However, there are signs of a return, with over 90 acquisitions in the AI space as of June 2021 according to the latest CB Insights data. With tech giants helping drive the demand for AI, smaller AI startups are becoming increasingly attractive targets for acquisition.

AI companies have their own set of specialized risks that may not be addressed if buyers approach the transaction with their standard process. AIs reliance on data and the dynamic nature of its insights highlight the shortcomings of standard agreement language and the risks in not tailoring agreements to address AI specific issues. Sophisticated parties should consider crafting agreements specifically tailored to AI and its unique attributes and risks, which lend the parties a more accurate picture of an AI systems output and predictive capabilities, and can assist the parties in assessing and addressing the risks associated with the transaction. These risks include:

Freedom to use training data may be curtailed by contracts with third parties or other limitations regarding open source or scraped data.

Clarity around training data ownership can be complex and uncertain. Training data may be subject to ownership claims by third parties, be subject to third-party infringement claims, have been improperly obtained, or be subject to privacy issues.

To the extent that training data is subject to use limitations, a company may be restricted in a variety of ways including (i) how it commercializes and licenses the training data, (ii) the types of technology and algorithms it is permitted to develop with the training data and (iii) the purposes to which its technology and algorithms may be applied.

Standard representations on ownership of IP and IP improvements may be insufficient when applied to AI transactions. Output data generated by algorithms and the algorithms themselves trained from supplied training data may be vulnerable to ownership claims by data providers and vendors. Further, a third-party data provider may contract that, as between the parties, it owns IP improvements, resulting in companies struggling to distinguish ownership of their algorithms prior to using such third-party data from their improved algorithms after such use, as well as their ownership and ability to use model generated output data to continue to train and improve their algorithms.

Inadequate confidentiality or exclusivity provisions may leave an AI systems training data inputs and material technologies exposed to third parties, enabling competitors to use the same data and technologies to build similar or identical models. This is particularly the case when algorithms are developed using open sourced or publicly available machine learning processes.

Additional maintenance covenants may be warranted because an algorithms competitive value may atrophy if the algorithm is not designed to permit dynamic retraining, or the user of the algorithm fails to maintain and retrain the algorithm with updated data feeds.

In addition to the above, legislative protection in the AI space has yet to fully mature, and until such time, companies should protect their IP, data, algorithms, and models, by ensuring that their transactions and agreements are specifically designed to address the unique risks presented by the use and ownership of training data, AI-based technology and any output data generated by such technology.

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Transactions in the Age of Artificial Intelligence: Risks and Considerations - JD Supra

A deep model was trained on historical crash data, road maps, satellite imagery, and GPS to enable high-resolution crash maps that could lead to safer roads.

Todays world is one big maze, connected by layers of concrete and asphalt that afford us the luxury of navigation by vehicle. For many of our road-related advancements GPS lets us fire fewer neurons thanks to map apps, cameras alert us to potentially costly scrapes and scratches, and electric autonomous cars have lower fuel costs our safety measures havent quite caught up. We still rely on a steady diet of traffic signals, trust, and the steel surrounding us to safely get from point A to point B.

To get ahead of the uncertainty inherent to crashes, scientists from MITs Computer Science and Artificial Intelligence Laboratory (CSAIL) and the Qatar Center for Artificial Intelligence developed a deep learning model that predicts very high-resolution crash risk maps. Fed on a combination of historical crash data, road maps, satellite imagery, and GPS traces, the risk maps describe the expected number of crashes over a period of time in the future, to identify high-risk areas and predict future crashes.

A dataset that was used to create crash-risk maps covered 7,500 square kilometers from Los Angeles, New York City, Chicago and Boston. Among the four cities, L.A. was the most unsafe, since it had the highest crash density, followed by New York City, Chicago, and Boston. Credit: Image courtesy of MIT CSAIL.

Typically, these types of risk maps are captured at much lower resolutions that hover around hundreds of meters, which means glossing over crucial details since the roads become blurred together. These maps, though, are 55 meter grid cells, and the higher resolution brings newfound clarity: The scientists found that a highway road, for example, has a higher risk than nearby residential roads, and ramps merging and exiting the highway have an even higher risk than other roads.

By capturing the underlying risk distribution that determines the probability of future crashes at all places, and without any historical data, we can find safer routes, enable auto insurance companies to provide customized insurance plans based on driving trajectories of customers, help city planners design safer roads, and even predict future crashes, says MIT CSAIL PhD student Songtao He, a lead author on a new paper about the research.

Even though car crashes are sparse, they cost about 3 percent of the worlds GDP and are the leading cause of death in children and young adults. This sparsity makes inferring maps at such a high resolution a tricky task. Crashes at this level are thinly scattered the average annual odds of a crash in a 55 grid cell is about one-in-1,000 and they rarely happen at the same location twice. Previous attempts to predict crash risk have been largely historical, as an area would only be considered high-risk if there was a previous nearby crash.

To evaluate the model, the scientists used crashes and data from 2017 and 2018, and tested its performance at predicting crashes in 2019 and 2020. Many locations were identified as high-risk, even though they had no recorded crashes, and also experienced crashes during the follow-up years. Credit: Image courtesy of MIT CSAIL.

The teams approach casts a wider net to capture critical data. It identifies high-risk locations using GPS trajectory patterns, which give information about density, speed, and direction of traffic, and satellite imagery that describes road structures, such as the number of lanes, whether theres a shoulder, or if theres a large number of pedestrians. Then, even if a high-risk area has no recorded crashes, it can still be identified as high-risk, based on its traffic patterns and topology alone.

To evaluate the model, the scientists used crashes and data from 2017 and 2018, and tested its performance at predicting crashes in 2019 and 2020. Many locations were identified as high-risk, even though they had no recorded crashes, and also experienced crashes during the follow-up years.

Our model can generalize from one city to another by combining multiple clues from seemingly unrelated data sources. This is a step toward general AI, because our model can predict crash maps in uncharted territories, says Amin Sadeghi, a lead scientist at Qatar Computing Research Institute (QCRI) and an author on the paper. The model can be used to infer a useful crash map even in the absence of historical crash data, which could translate to positive use for city planning and policymaking by comparing imaginary scenarios.

The dataset covered 7,500 square kilometers from Los Angeles, New York City, Chicago, and Boston. Among the four cities, L.A. was the most unsafe, since it had the highest crash density, followed by New York City, Chicago, and Boston.

If people can use the risk map to identify potentially high-risk road segments, they can take action in advance to reduce the risk of trips they take. Apps like Waze and Apple Maps have incident feature tools, but were trying to get ahead of the crashes before they happen, says He.

Reference: Inferring high-resolution traffic accident risk maps based on satellite imagery and GPS trajectories by Songtao He, Mohammad Amin Sadeghi, Sanjay Chawla, Mohammad Alizadeh, Hari Balakrishnan and Samuel Madden, ICCV.PDF

He and Sadeghi wrote the paper alongside Sanjay Chawla, research director at QCRI, and MIT professors of electrical engineering and computer science Mohammad Alizadeh, ??Hari Balakrishnan, and Sam Madden. They will present the paper at the 2021 International Conference on Computer Vision.

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Predicting Traffic Crashes Before They Happen With Artificial Intelligence - SciTechDaily

Despite AIs promise, many companies are not realizing the full potential of machine learning and other AI functions. Why? Ironically, it turns out that the issue is, in large part...people. Inefficient workflows can hold companies back from getting the full value of their AI implementations.

For example, data scientists can face challenges getting the resources and data they need to build machine learning models. They may have trouble collaborating with their teammates. And they have many different open source tools to manage, while application developers sometimes need to entirely recode models that data scientists develop before they can embed them into their applications.

With a growing list of open source AI tools, IT ends up spending more time supporting the data science teams by continuously updating their work environments. This issue is compounded by limited standardization across how data science teams like to work.

Finally, senior executives might not be able to visualize the full potential of their companys AI investments. Consequently, they dont lend enough sponsorship and resources to creating the collaborative and integrated ecosystem required for AI to be successful.

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What is Artificial Intelligence (AI)? - Oracle

DUBLIN, October 13, 2021--(BUSINESS WIRE)--The "Artificial Intelligence Market, Global Forecast, Impact of COVID-19, Industry Trends, by Solution, Technology, Region, Opportunity Company Analysis" report has been added to ResearchAndMarkets.com's offering.

According to the report, the global AI market will be worth US$ 284.4 billion by 2026.

Today, the artificial intelligence platform has become a way for computer systems to perform tasks like human intelligence including decision-making and speech recognition. Globally, problem-solving, social intelligence and general intelligence is being achieved with the help of the artificial intelligence platform. Moreover, rising high-level computer languages is helping various industries to work efficiently on the artificial intelligence platform.

By Solution

The Artificial Intelligence Market revolves around hardware, software and services. In recent years, artificial intelligence services are at the forefront of all innovations and will continue to remain so during the forecast years. Artificial intelligence services cover installation, integration, maintenance & support projects. With the escalating abundance of enterprises and competition, companies have rigorously integrated artificial intelligence (AI) technology into their services. For instance, the BFSI industry has increasingly adopted artificial intelligence services to enhance operational efficiency and enable a rich consumer experience.

Besides, software solutions promise advancements in information storage capacity, high computing power, and parallel processing capabilities to achieve high-end artificial intelligence software in dynamic end-use verticals. Notwithstanding, artificial intelligence software solutions include libraries for designing and deploying artificial intelligence applications, such as linear algebra, primitives, inference, video analytics, sparse matrices, and multiple hardware communication capabilities. As per the analysis, Global Artificial Intelligence Industry is anticipated to expand at a tremendous CAGR of 29.44% during the forecast period 2020-2026.

Story continues

By Chip Type

Artificial intelligence includes chipsets such as Graphics Processing Unit (GPU), Application-Specific Integrated Circuit (ASIC), Field-Programmable Gate Array (FPGA) and Central Processing Unit (CPU). An application-specific integrated circuit (ASIC) dominates the artificial intelligence chip segment, specially built for a specific application or purpose. It is specifically designed for a particular application compared to a logic device or a standard logic integrated circuit. It has also been made smaller, and it uses less electricity.

The artificial intelligence market inculcates machine learning, natural language processing, image processing, and speech recognition by technology. Machine learning covers notable investments in artificial intelligence. It covers both artificial intelligence platforms and cognitive applications, including tagging, clustering, categorization, hypothesis generation, alerting, filtering, navigation, visualization, facilitating advisory, intelligent, and cognitively equipped solutions. As per the estimation, Worldwide Artificial Intelligence Market Size was US$ 60.46 Billion in 2020.

Geographic Analysis of Artificial Intelligence Trends

The worldwide artificial intelligence market is segmented into North America, Europe, Asia-Pacific, Latin America, the Middle East and Africa. North America is a significant contributor owing to rising government initiatives and investments in the market. Further, in the Asia Pacific, China demonstrates increasing investments in artificial intelligence technology to provide robust results. Likewise, India can gain traction due to the government's pilot project to implement artificial intelligence in the agriculture and healthcare industries. The rest of Asia Pacific countries like Bangladesh, Vietnam, and Indonesia are also expected to grow at a good pace.

Company Analysis

Globally, artificial intelligence vendors have implemented various types of organic and inorganic growth strategies, such as new product launches, product upgrades, alliances and affiliations, mergers and acquisitions to strengthen their offerings in the market. The major companies in the global Artificial Intelligence market include Google Inc., IBM Corp., Microsoft Corporation, Baidu Inc., Xilinx, Inc., Cisco Systems, Inc., Nvidia Corporation and Intel Corporation.

COVID-19 Analysis

Although the novel coronavirus outbreak pandemic has caused a massive impact on businesses and humankind. Still, the pandemic has emerged as an opportunity for Artificial Intelligence Market to fight against the epidemic. Numerous tech giants and start-ups are operating on barring mitigating and containing the virus. Furthermore, the COVID-19 outbreak is foreseen to spur the market germination of next-generation tech domains, including artificial intelligence, owing to the mandated work-from-home (WFH) policy due to this pandemic.

Also, tech companies are extending their product offerings and assistance to broaden availability across the globe. For instance, in April 2020, Google LLC launched an AI-enabled chatbot called Rapid Response Virtual Agent concerning call centres. This chatbot is created to respond to concerns customers might be experiencing due to the COVID-19 outbreak over voice, chat, and other social channels.

Company Analysis

Google Inc.

IBM Corp.

Microsoft Corporation

Baidu Inc.

Xilinx, Inc.

Cisco Systems, Inc.

Nvidia Corporation

Intel Corporation

Key Topics Covered

1. Introduction

2. Research Methodology

3. Executive Summary

4. Market Dynamics

4.1 Growth Drivers

4.2 Challenges

5. Global Artificial Intelligence Market

6. Market Share

6.1 By Solution

6.2 By Technology

6.3 By Chip Type

6.4 By Region

7. Solution - Global Artificial Intelligence Market

7.1 Hardware

7.2 Software

7.3 Services

8. Technology - Global Artificial Intelligence Market

8.1 Machine Learning

8.2 Natural Language Processing

8.3 Image Processing

8.4 Speech Recognition

9. Chip Type - Global Artificial Intelligence Market

9.1 Graphics Processing Unit (GPU)

9.2 Application-Specific Integrated Circuit (ASIC)

9.3 Field-Programmable Gate Array (FPGA)

9.4 Central Processing Unit (CPU)

9.5 Others

10. Region - Global Artificial Intelligence Market

10.1 North America

10.2 Europe

10.3 Asia-Pacific

10.4 Latin America

10.5 Middle East and Africa

11. Company Analysis

11.1 Overview

11.2 Recent Developments

11.3 Financial Insight

For more information about this report visit https://www.researchandmarkets.com/r/rfvj6v

View source version on businesswire.com: https://www.businesswire.com/news/home/20211013005577/en/

Contacts

ResearchAndMarkets.comLaura Wood, Senior Press Managerpress@researchandmarkets.com For E.S.T Office Hours Call 1-917-300-0470For U.S./CAN Toll Free Call 1-800-526-8630For GMT Office Hours Call +353-1-416-8900

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Detailed Analysis of the Global Artificial Intelligence Market, 2021-2026 - ResearchAndMarkets.com - Yahoo Finance

Pixabay

To say that AI is complicated is an understatement. Machine learning, a subset of artificial intelligence, is a multifaceted process that integrates and scales mountains of data that comes in different forms from various sources. Data is used to train machine learning models in order to develop insights and solutions from newly acquired related data. For example, an image recognition model trained with several million dog and cat photos can efficiently classify a new image as either a cat or a dog.

A better way to build and manage machine learning models

Project Codeflare

The development of machine learning models requires the coordination of many processes linked together with pipelines. Pipelines can handle data ingestion, scrubbing, and manipulation from varied sources for training and inference.Machine learning models use end-to-end pipelines to manage input and output data collection and processing.

To deal with the extraordinary growth of AI and its ever-increasing complexity, IBM created an open-source framework calledCodeFlareto deal with AIs complex pipeline requirements. CodeFlaresimplifies the integration, scaling, and acceleration of complex multi-step analytics and machine learning pipelines on the cloud.Hybrid cloud deployment is one of the critical design points for CodeFlare, which using OpenShift can be easily deployed from on-premises to public clouds to edge.

It is important to note thatCodeFlare is not currently a generally available product, and IBM has yet to commit to a timeline for it becoming a product. Nevertheless, CodeFlare is available as an open-source project.And, as an evolving project, some aspects of orchestration and automation are still work in progress. At this stage, issues can be reported through the public GitHub project. IBM invites community engagement through issue and bug reports, which will be handled on a best effort basis.

CodeFlares main features are:

Technology

CodeFlare is built on top of Ray, an open-source distributed computing framework for machine learning applications. According to IBM, CodeFlare extends the capabilities of Ray by adding specific elements to make scaling workflows easier. CodeFlare pipelines run on a serverless platform using IBM Cloud Code Engine and Red Hat OpenShift. This platform providesCodeFlare the flexibility to be deployed just about anywhere.

Emerging workflows

Emerging AI/ML workflows pose new challenges

CodeFlare can integrate emerging workflows with complex pipelines that require integration and coordination of different tools and runtimes. It is designed also to scale complex pipelines such as multi-step NLP, complex time series and forecasting, reinforcement learning, and AI-Workbenches. The framework can integrate, run, and scale heterogenous pipelines that use data from multiple sources and require different treatments.

How much difference does CodeFlare make?

According to theIBM Research blog, CodeFlare significantly increases the efficiency of machine learning. The blog states that a user used the framework to analyze and optimize approximately 100,000 pipelines for training machine learning models. CodeFlare cut the time it took to execute each pipeline from 4 hours to 15 minutes - an 18x speedup provided by CodeFlare.

The research blog also indicates that CodeFlare can save scientists months of work on large pipelines, providing the data team more time for productive and development work.

Wrapping up

Studies show that about75%of prototype machine learning models fail to transition to production status despite large investments in artificial intelligence. Several reasons for low conversion rates range from poor project planning to weak collaboration and communications between AI data team members.

CodeFlare is a purpose-built platform that provides complete end-to-end pipeline visibility and analytics for a broad range of machine learning models and workflows. It providesa more straightforward way to integrate and scale full pipelines while offering a unified runtime and programming interface.

For those reasons, despite the historical high AI model failure rates, Moor Insights & Strategy believes that machine learning models using CodeFlare pipelines will have a high percentage of machine learning models transition from experimental status to production status.

Analyst Notes:

Note: Moor Insights & Strategy writers and editors may have contributed to this article.

Moor Insights & Strategy, like all research and analyst firms, provides or has provided paid research, analysis, advising, or consulting to many high-tech companies in the industry, including 8x8, Advanced Micro Devices, Amazon, Applied Micro, ARM, Aruba Networks, AT&T, AWS, A-10 Strategies,Bitfusion, Blaize, Box, Broadcom, Calix, Cisco Systems, Clear Software, Cloudera,Clumio, Cognitive Systems, CompuCom, Dell, Dell EMC, Dell Technologies, Diablo Technologies, Digital Optics,Dreamchain, Echelon, Ericsson, Extreme Networks, Flex, Foxconn, Frame (now VMware), Fujitsu, Gen Z Consortium, Glue Networks, GlobalFoundries, Google (Nest-Revolve), Google Cloud, HP Inc., Hewlett Packard Enterprise, Honeywell, Huawei Technologies, IBM, Ion VR,Inseego, Infosys, Intel, Interdigital, Jabil Circuit, Konica Minolta, Lattice Semiconductor, Lenovo, Linux Foundation,MapBox, Marvell,Mavenir, Marseille Inc, Mayfair Equity, Meraki (Cisco),Mesophere, Microsoft, Mojo Networks, National Instruments, NetApp, Nightwatch, NOKIA (Alcatel-Lucent), Nortek,Novumind, NVIDIA, Nuvia, ON Semiconductor, ONUG, OpenStack Foundation, Oracle, Poly, Panasas,Peraso, Pexip, Pixelworks, Plume Design, Poly,Portworx, Pure Storage, Qualcomm, Rackspace, Rambus,RayvoltE-Bikes, Red Hat,Residio, Samsung Electronics, SAP, SAS, Scale Computing, Schneider Electric, Silver Peak, SONY,Springpath, Spirent, Splunk, Sprint, Stratus Technologies, Symantec, Synaptics, Syniverse, Synopsys, Tanium, TE Connectivity,TensTorrent,TobiiTechnology, T-Mobile, Twitter, Unity Technologies, UiPath, Verizon Communications,Vidyo, VMware, Wave Computing,Wellsmith, Xilinx, Zebra,Zededa, and Zoho which may be cited in blogs and research.

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Create And Scale Complex Artificial Intelligence And Machine Learning Pipelines Anywhere With IBM CodeFlare - Forbes