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What are the best robotics and artificial intelligence stocks to buy today? In this time of uncertainty characterized by volatile market movements, economic contraction, and spiraling unemployment, finding stocks to put your money into seems like an arduous task. Some investors might think that the stock market is acting irrationally and puzzled by the quick recovery of stock prices sin the end of March. The market's movements isn't far away from economic realities. Economic reality is that long-term real interest rates are negative, the Federal Reserve is flooding the market with cheap credit, and the current economic slowdown is temporary.

This is the perfect environment to buy technology stocks which aren't negatively affected by the coronavirus induced lockdowns and economic slowdown. In this article we are going to take a look at the top 5 robotics and artificial intelligence stocks to buy. We are on the cusp of a technological revolution that will fundamentally change how we live our lives. Recent advancements in machine learning and artificial intelligence will open the door to robots, driving cars, and many other inventions that we can't even imagine today. So, we decided to take a look at the best robotics and AI stocks to buy in order to generate high returns as the companies bring new products in to the marketplace.

robotics and AI stocks

In order to compile this list of best robotics and AI stocks to buy we started with top 15 stocks in the Global X Robotics & Artificial Intelligence ETF (BOTZ). According to its website this ETF "seeks to invest in companies that potentially stand to benefit from increased adoption and utilization of robotics and artificial intelligence (AI), including those involved with industrial robotics and automation, non-industrial robots, and autonomous vehicles".

Savvy investors have used hedge funds as a litmus test to gauge the profitability of stocks and to know the trajectory of market sentiment. Research carried out by Insider Monkey has shown that a select group of hedge fund holdings have consistently outperformed the S&P 500 ETFs by more than 56 percentage points since March 2017 (see the details here). As such, hedge fund sentiments are undoubtedly a useful indicator that experienced investors should pay attention to.

Based on hedge funds sentiment, we present 5 most popular robotics and AI stocks among the 800+ hedge funds tracked by Insider Monkey.

5. John Bean Technologies Corporation (NYSE:JBT)

John Bean Technologies Corporation (NYSE:JBT) provides technology solutions to the food and beverage industry, including equipment and services to air transportation industries. The company has a market capitalization of $2.995bn. This stock has underperformed by -18.4%. In 2020, Q1 John Bean Technologies Corporation (NYSE:JBT) released quarterly earnings of $1.09 per share. This compares to earnings of $1.42 per share a year ago

The company is poised to gain from focus on developing innovative products and services and expanding the aftermarket business on the effects of the pandemic are over. The management also aims to continue its Elevate Plan aiming to drive persistent growth and margin expansion and strategic acquisition programs. Growing demand for protein, beverages and ready-to-eat meals are likely to act as key catalysts in the long haul.

John Bean Technologies Corporation (NYSE:JBT) is in the portfolio of 12 hedge funds. Royce Associates has the biggest position in JBT in our database. Adage Capital and Citadel are also invested in this stock but they have been trimming their holdings more recently.

4. Brooks Automation, Inc. (NASDAQ:BRKS)

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Brooks Automation, Inc. (NASDAQ:BRKS) has a market capitalization of $3.49bn. This year, the company's share price has increased by 10.84%. In 2020,

Brooks Automation is in the portfolio of 18 hedge funds. Billionaire Ken Fisher, Chuck Royce, and Paul Marshall And Ian Wace are among the top hedge fund backers of BRKS.

3. iRobot Corporation (NASDAQ:IRBT)

iRobot Corporation (NASDAQ:IRBT) is a robotics company with a market capitalization of $2.052bn. Its share price has increased by 51.3% this year. iRobot Corporation (NASDAQ: IRBT) maintained its earnings streak in the second quarter of 2020, reporting earnings of $1.06 per share.

It is no surprise that the company is in the portfolio of 19 hedge funds. Quant hedge fund Two Sigma has the biggest equity position in IRBT in our database.

2. Intuitive Surgical, Inc. (NASDAQ:ISRG)

Intuitive Surgical, Inc. (NASDAQ:ISRG) is known for its da Vinci surgical systems in the United States and internationally. The company has a market capitalization of $80.443B. This yeas alone, shares of Intuitive Surgical, Inc. (NASDAQ:ISRG) have increased by 15%.

Even though the company reported adjusted earnings per share (EPS) of $1.11, for the second quarter, its earnings collapsed 65.8% year over year.

Fifty hedge funds had bullish positions in Intuitive Surgical, Inc. (NASDAQ:ISRG) at the end of the first quarter. The largest stake in Intuitive Surgical, Inc. (NASDAQ: ISRG) is held by Fisher Asset Management, which reported holding $350.1 million worth of stock at the end of September. It was followed by GQG Partners with a $137.2 million position. Other investors bullish on the company included Citadel Investment Group, Adage Capital Management, and OrbiMed Advisors. In terms of the portfolio weights assigned to each position, Unio Capital allocated the biggest weight to Intuitive Surgical, Inc. (NASDAQ: ISRG), around 3.45% of its 13F portfolio. Rock Springs Capital Management is also relatively bullish on the stock, designating 3.12 percent of its 13F equity portfolio to ISRG.

1. NVIDIA Corporation (NASDAQ:NVDA)

NVIDIA Corporation (NASDAQ:NVDA) is a gaming and crypto company. We discussed NVDA in detail in this article. The company has a market capitalization of $261.104bn. NVIDIA has been one of the best performers in the U.S. stock market over the last few years. The stock was changing hands for $30 at the beginning of 2016 and currently trades at $440, representing a more than thirteen-fold jump. The company recently surpassed Intel (NASDAQ:INTC) to become the largest U.S. semiconductor maker.

Based on this performance, it is no surprise that the stock is in the portfolio of 95 hedge funds. Fisher Asset Management and GQG Partners held the largest equity positions in NVDA in our database at the end of March.

Disclosure: None. This article is originally published at Insider Monkey.

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Top 5 Robotics and Artificial Intelligence Stocks To Buy According to Hedge Funds - Yahoo Finance

Global Artificial Intelligence in Medical Imaging Market of which artificial intelligence in medical imaging is a part of is expected to rise from its initial estimated value of USD 21.48 billion in 2018 to a projected value of USD 264.85 billion by 2026, registering a CAGR of 36.89% in the forecast period of 2019-2026.

Medical imaging can be described as the diagnostic procedure that involves the creation of visual aids and image representations of the human body, and involves the monitoring of performance and functioning of the organs of the human body. With the integration of artificial intelligence (AI) in healthcare and medical imaging, there is a change in the way the diagnostics and the entire procedure is carried out. The AI assists the surgeons in carrying out the image capturing process and how to diagnose these images for the conclusion and personalized treatment in respect to every individual and patient. Artificial intelligence mainly consists of two types, robots and machine learning.

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Global artificial intelligence in medical imaging market is expected to grow with a significant CAGR in the forecast period of 2019-2026. The report contains data from the base year of 2018, and the historic year of 2017. This rise in market value can be attributed to better visualization and conclusive results in diagnostic procedure with the application of artificial intelligence in medical imaging.

Market Drivers:

Market Restraints:

By Technology

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Key Developments in the Market:

Global artificial intelligence in medical imaging market is highly fragmented and the major players have used various strategies such as new product launches, expansions, agreements, joint ventures, partnerships, acquisitions, and others to increase their footprints in this market. The report includes market shares of artificial intelligence in medical imaging market for global, Europe, North America, Asia-Pacific, South America and Middle East & Africa.

Major competitors currently present in the market are BenevolentAI, OrCam, Babylon, Freenome Inc., Clarify Health Solutions, BioXcel Therapeutics, Ada Health GmbH, GNS Healthcare, Zebra Medical Vision Inc., Qventus Inc, IDx Technologies Inc., K Health, Prognos, Medopad Ltd., Viz.ai Inc., Voxel Technology, Renalytix AI plc, Beijing Pushing Technology Co. Ltd., PAIGE, mPulse Mobile, Suki AI Inc., BERG LLC, Zealth Inc., OWKIN INC., and Your.MD.

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Artificial Intelligence in Medical Imaging Market Size Restraint,Bussiness Oppertunity With Leading Player | Qventus Inc, IDx Technologies Inc., K...

The dream of creating a machine that emulates human behavior has been an obsession throughout human history. Artificial Intelligence (AI) has been in our minds for many years, since Adam's creation: "God creates him from a moldable material, programs him, and gives him the first instructions (Snchez-Martn et al. 2007)." Even in Greek mythology with Ovid's account of Pygmalion sculpting a figure of a beautiful woman who is given life for Pygmalion to love her. In Hebrew mythology, the Golem was created with clay and animated to save the inhabitants of a Jewish city. In Norse mythology, the giant Mkkurklfi or Mistcalf was created from clay to support the troll Hrungnir in his fight against Thor. In each epoch, the examples continue.

Artificial Intelligence (AI), in its most natural sense, is about how to simulate the capabilities of human brain intelligence, so thinking about AI is also thinking about what makes it possible for us to interact and learn. Its applications can contribute significantly to education (Ocaa-Fernndez, Valenzuela-Fernndez, and Garro-Aburto, 2019).

The COVID-19 pandemic has provoked substantial educational changes, among them the migration to virtual learning ecosystems. Teachers must confront the task of attending a wide variety of needs to ensure that students' education continues. Artificial intelligence can be ideal pedagogical support to facilitate attention to our students at any time. Imagine how it can help you respond to each student's questions in real-time while being confident that the student is being oriented correctly. Also, you can take advantage of that time to study some topics of interest, deepen the development of your class, conduct research, build teaching sequences, and perform Mindfulness activities to potentiate your creativity and innovation, to cite some ideas. Wouldn't this be fabulous?

The main objective is to provide our colleagues with the opportunity to build an intelligent pedagogical assistant through a chatbot, which contributes to solving a large part of the students' concerns. The structuring of the responses was designed with the flipped learning approach to provide feedback on class concerns.

What are the real possibilities of applying AI in education? Could AI be a key component in a new educational model? Can you imagine having a colleague who helps us answer hundreds of common questions from our students around the clock or updating anyone who could not connect to the class on time? You probably think that this means having an assistant advisor or a teacher's assistant. Well, this is not so far from our reach.

The journey of artificial intelligence began with Alan Turing in 1936 with the publication of his famous article, "On Computable Numbers, with an Application to The Entscheidungsproblem." The paper established the bases of theoretical computing and the origin of the concept "Turing Machine," which formalized the algorithm concept that would become the precursor process of digital computers. In 1956, at the mythical Dartmouth conference, John McCarthy, Marvin Minsky, and Claude Shannon coined the term "Artificial Intelligence." Even though there was much positive speculation about this technology, AI indeed jumped on the world stage in1997 when the IBM computer, Deep Blue, beat world-chess-champion Gari Kasparov. A profound reflection on its potential began in different fields, like science fiction, computer science, mathematics, social sciences, and even humanities.

A little later, the computer Watson, also from IBM, would win a duel against the human brain in "Jeopardy," the famous quiz show of questions and answers on the American television network, ABC. Isaac Asimov wrote the eminent three laws of robotics that brought us closer to thinking about the ethical problems that the development of artificial intelligence brings us so that we might avoid the revelations of science fiction like that of Hal 9000 in 2001: A Space Odyssey.

In recent years we have seen significant progress. In March 2019, the High-Level Expert Group on AI (AI HLEG), a steering group for the European AI Alliance, drew up a draft of AI ethical guidelines that help us understand the relevance of this topic being attended not only in the area of technology but also in the social sciences and humanities.

Artificial Intelligence can be categorized into three levels that allow us to locate ourselves as we navigate the continuum of incremental innovation, starting with incorporating this technology into our daily lives, especially in education.

Level 1: Revolutionary. Big technology companies such as Google, Microsoft, and Hanson Robotics seek to improve living conditions in everyday life and affect our home, cars, food, and health. An example of this is Google's supercomputer and Sophia, the humanoid robot.

Level 2: Expansion. At this level, AI is used to boost production to a larger scale in areas such as communication, the everyday market, and risk analysis on the stock exchange. An example of this is Amazon's machine learning systems.

Level 3: Communication. At this level appear the fundamental processes of interaction with free software that seeks to respond to users' needs either by programming or emulating mechanical learning of the likely responses that are helpful. Examples include natural language comprehension platforms such as Dialogflow, Botmake.io, Cliengo, Snathbot.me, and Manychat.

In education, level 3 tools are alternatives that respond to teaching needs. In particular, a tool we can call chatbot, platforms that understand natural language, and allow the programming of automatic responses emulate human conversations.

At the University of the East in Mexico, we use the Dialogflow tool for processes oriented to our students' accompaniment with significant advantages that I share below.

The main objective is to encourage our colleagues to take advantage of the opportunity to build a pedagogical assistant that helps to resolve many of the students' concerns. The structuring of the responses was explicitly geared to the flipped learning approach, which facilitates feedback to the students about their interests. This approach benefits the students by readily available answers and referring them to multimedia reference sources that extend and improve their experience.

We decided to load the application on Moodle, the institutional platform for academic reinforcement, to ensure that the pedagogical assistants were customized to the classes' needs. The desired results of this implementation were to equip our teachers with more competitive and functional tools to support our students in accompanied activities within a context of constant communication. The main challenge for those participating in this project is to ensure that the responses are much more dynamic and lead to more meaningful contributions.

The academic work with this type of chat allows us, in addition to maintaining a relationship of communication with our students, to link the conversation to other tools that help our students confront challenges through learning capsules that deepen or engage them in contexts of professional development.

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How to Apply Artificial Intelligence in Education? - Observatory of Educational Innovation

Artificial Intelligence (AI) has been one of the biggest technology success stories of the past decade. As the COVID-19 pandemic spread across the globe, researchers and entrepreneurs stepped up to devise new ways to combat it. From understanding and preventing the spread of the virus to diagnosing and treating it, startups and established technology companies in India are actively leveraging AI to support this fight.

Modeling the pandemic

Decision makers have increasingly relied on computer simulations to understand how the pandemic situation will evolve over time. TCS, in collaboration with Pune-based Prayas Health Group, is using digital twins to forecast the spread of COVID-19 in urban districts. A digital twin is a virtual computerised model of a physical system that takes real-world data as input and predicts the future evolution of the system.

"Macro models don't work well in countries like India which have high heterogeneity. So we developed ward-level digital twins that modelled the spread of the disease as a function of the number of proximal contacts, average duration of contacts, people and place characteristics, and population demographics like age, gender, comorbidities, etc, says Vinay Kulkarni, distinguished chief scientist, TCS. The model predictions closely match the observations reported by city corporations and empower the administration to take better locality-specific decisions."

Prevention

Ensuring people wear face masks and follow social distancing is expected to be a major challenge for organisations. AI is being used to monitor live CCTV feeds and instantly report violations of guidelines to safety administrators.

It is critical that a safe ecosystem is created for businesses and schools to re-open as soon as possible in spite of COVID-19, says Atul Arya, CEO of Blackstraw, an AI company. Our AI-powered health risk management system developed jointly by Blackstraw and Bharat Forge not only enables safety of humans and compliance with government guidelines, but also drives long-term behavioural changes that are crucial to live by in the new normal.

Diagnosis

The reverse transcription polymerase chain reaction (RT-PCR) test is considered the gold standard for COVID-19 diagnosis. Due to long processing times, many hospitals use chest X-ray and CT scans for screening patients. Radiology scans are also useful in monitoring progression of the disease and assessing the degree of lung infection. AI has made rapid advances in the last few years in diagnosing tuberculosis amongst other pathologies from radiology images. Enterprising medical startups were quick to repurpose their TB solutions for COVID-19.

We do not believe AI can yet replace radiologists. Our AI solutions are, instead, designed to augment and assist them, says Dr Amit Kharat, a radiologist and co-founder at DeepTek. Our AI models have been used in the field to report over 80,000 X-rays for a government-run TB screening program. We are using the same base technology for COVID-19 screening.

By decreasing radiologist efforts and improving reporting times, such AI-enabled solutions help in making timely and accurate diagnosis affordable for everyone.

Treatment

TCS is using AI simulations to synthesise molecules and discover new drugs to fight the virus. From a candidate set of 50,000 molecules, their simulations selected 31 molecules that are now undergoing trials as potential cures.

A new untested drug needs to pass extensive clinical trials on human patients before approval. Hence, the short-term focus of pharmaceutical companies has been on repurposing existing drugs that have already cleared trials for COVID-19 treatment. Innoplexus is using AI to crunch through a massive dataset of available drugs to identify safe drugs that could disrupt the functioning of the virus.

Regulations and policy

Hospitals and governments have tempered their enthusiasm for AI with caution. AI systems are brittle and work only on the specific datasets that were used to build them. They need to be extensively tested on real-world data before being adopted in practice.

It is difficult to trust AI since it cannot explain its predictions, says Sahil Deo, an AI-policy expert and co-founder of CPC Analytics. There is also the question of who is to be held accountable if the AI mispredicts. We need a strong regulatory framework before we can widely adopt AI in decision making.

(The author has a masters degree in computer science from University of California, Berkeley and is currently pursuing a Ph.D. in Quantum Artificial Intelligence)

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How India is using Artificial Intelligence to combat COVID-19 - THE WEEK

Amazon has a project they call Rekognition. Its an AI-based facial recognition software thats marketed to police agencies for use in investigations. Its essentially supposed to cross analyze images and direct law enforcement officers to possible suspects. The problem is that its not very accurate.

In a study by the Massachusetts chapter of the ACLU, dozens of Boston-area athletes pictures were run through the system. At least 27 of these athletes or roughly one-in-six were falsely matched with mugshots. This included three-time Super Bowl champion Duron Harmon of the New England Patriots.

Can you say, not a good look?

Users Find Flaws in Apples Face ID

Apple is always coming up with cutting edge technology. Theyve set the standards in the smartphone and mobile device industry for years. For the most part, they get things right. But sometimes they can be a bit too brash in their marketing. In other words, they like to flex their muscles. As you might expect, this invites haters, trolls, and skeptics to challenge their claims.

One recent example occurred with the release of the iPhone X. Leading up to the launch, Apple had invested a lot of time and marketing dollars into their front-facing facial recognition system that replaced the fingerprint reader as the primary method of accessing the phone. The claim was that the AI component was so smart readers could wear glasses, makeup, etc. without compromising functionality. And thats essentially true. The problem is that Apple also clearly stated the Face ID technology cant be spoofed by masks or other techniques.

One Vietnam-based security firm took this as a challenge. And with just $200, they made a mask out of stone powder, glued on some printed 2D eyes, and unlocked a phone. This is just a reminder that bold claims can sometimes come back to bite!

Robot Dog Meets Fatal Ending

Who doesnt love the idea of a robot puppy? You get a cute little machine without the barking, walking, pooping, eating, or expensive vet bills. But if youre looking for a life partner, you might not want this robodog.

In 2019, a Boston Robotics robodog named Spot met a dramatic and untimely onstage death while he was being demoed by the company CEO at a conference in Las Vegas. Tasked with walking, he slowly started to stumble and eventually collapsed to the floor as the audience uncomfortably gasped and chuckled.

Watson Is Not a Doctor

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Examples of Failure in Artificial Intelligence - ReadWrite