Category Archives: Artificial Intelligence

The U.S. Food and Drug Administration (FDA) issued the Artificial Intelligence/Machine Learning (AI/ML)-Based Software as a Medical Device (SaMD) Action Plan from the Center for Devices and Radiological Healths Digital Health Center of Excellence.

The Action Plan is a direct response to stakeholder feedback to the April 2019 discussion paper, Proposed Regulatory Framework for Modifications to Artificial Intelligence/Machine Learning-Based Software as a Medical Device and outlines five actions the FDA intends to take.

Download Action Plan (PDF - 747 KB)

Artificial intelligence and machine learning technologies have the potential to transform health care by deriving new and important insights from the vast amount of data generated during the delivery of health care every day. Medical device manufacturers are using these technologies to innovate their products to better assist health care providers and improve patient care.

The FDAs Center for Devices and Radiological Health (CDRH) is considering a total product lifecycle-based regulatory framework for these technologies that would allow for modifications to be made from real-world learning and adaptation, while ensuring that the safety and effectiveness of the software as a medical device are maintained.

Artificial Intelligence has been broadly defined as the science and engineering of making intelligent machines, especially intelligent computer programs (McCarthy, 2007). Artificial intelligence can use different techniques, including models based on statistical analysis of data, expert systems that primarily rely on if-then statements, and machine learning.

Machine Learning is an artificial intelligence technique that can be used to design and train software algorithms to learn from and act on data. Software developers can use machine learning to create an algorithm that is locked so that its function does not change, or adaptive so its behavior can change over time based on new data.Some real-world examples of artificial intelligence and machine learning technologies include:

Artificial intelligence (AI) and machine learning (ML) technologies have the potential to transform health care by deriving new and important insights from the vast amount of data generated during the delivery of health care every day. Medical device manufacturers are using these technologies to innovate their products to better assist health care providers and improve patient care. One of the greatest benefits of AI/ML in software resides in its ability to learn from real-world use and experience, and its capability to improve its performance.

Traditionally, the FDA reviews medical devices through an appropriate premarket pathway, such as premarket clearance (510(k)), De Novo classification, or premarket approval. The FDA may also review and clear modifications to medical devices, including software as a medical device, depending on the significance or risk posed to patients of that modification. Learn the current FDA guidance for risk-based approach for 510(k) software modifications.

The FDAs traditional paradigm of medical device regulation was not designed for adaptive artificial intelligence and machine learning technologies. Under the FDAs current approach to software modifications, the FDA anticipates that many of these artificial intelligence and machine learning-driven software changes to a device may need a premarket review.

On April 2, 2019, the FDA published a discussion paper Proposed Regulatory Framework for Modifications to Artificial Intelligence/Machine Learning (AI/ML)-Based Software as a Medical Device (SaMD) - Discussion Paper and Request for Feedback that describes the FDAs foundation for a potential approach to premarket review for artificial intelligence and machine learning-driven software modifications.

The ideasdescribed in thediscussion paper leverage practices from our current premarket programs and rely on IMDRFs risk categorization principles, the FDAs benefit-risk framework, risk management principles described in the software modifications guidance, and the organization-based total product lifecycle approach (also envisioned in the Digital Health Software Precertification (Pre-Cert) Program).

In the framework described in the discussion paper, the FDA envisions a predetermined change control plan in premarket submissions. This plan would include the types of anticipated modificationsreferred to as the Software as a Medical Device Pre-Specificationsand the associated methodology being used to implement those changes in a controlled manner that manages risks to patients referred to as the Algorithm Change Protocol.

In this potential approach, the FDA would expect a commitment from manufacturers on transparency and real-world performance monitoring for artificial intelligence and machine learning-based software as a medical device, as well as periodic updates to the FDA on what changes were implemented as part of the approved pre-specifications and the algorithm change protocol.

Such a regulatory framework could enable the FDA and manufacturers to evaluate and monitor a software product from its premarket development to postmarket performance. This approach could allow for the FDAs regulatory oversight to embrace the iterative improvement power of artificial intelligence and machine learning-based software as a medical device, while assuring patient safety.

As part of the AI/ML Action Plan, the FDA is highlighting its intention to develop an update to the proposed regulatory framework presented in the AI/ML-based SaMD discussion paper, including through the issuance of a draft guidance on the predetermined change control plan.

If you have questions about artificial intelligence, machine learning, or other digital health topics, ask a question about digital health regulatory policies.

McCarthy, J. (2007). What Is Artificial Intelligence? Stanford University, Stanford, CA. Retrieved from http://jmc.stanford.edu/articles/whatisai/whatisai.pdf

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Artificial Intelligence and Machine Learning in Software ...

For overwhelmed IT teams, AIOps holds the promise of automatically heading off potential business impacting outages. But some IT leaders are skeptical about whether it can really deliver results.

Rodrigo de la Parra, AIOps Domain Leader at IBM Automation, addressed that skepticism at a recent CanadianCIO virtual roundtable. Its more than a buzzword, said de la Parra, AIOps takes IT to a more software-driven, agile approach.

AIOps is the application of artificial intelligence to enhance IT operations, explained de la Parra. It spots issues by using machine learning to analyze huge amounts of data generated by tools across an organizations infrastructure. Automation and natural language processing can be leveraged to help fix problems in real-time.

Its not a product or a single solution, said de la Parra. Its a journey. To unlock the value, he said its essential to align AIOps to support business needs for improved efficiency and customer service.

De la Parra distinguished between what he referred to as domain specific and domain agnostic tools. He noted that the domain specific tools had great value within their specific silo. But the real value, de la Parra said, comes from adding a domain agnostic approach because it can take feeds from all the tools running in silos and produce a single data source. This becomes the single source of truth for the analytics and to provide evidence on the root cause to the stakeholders, said de la Parra.

Successful implementation starts with an operational assessment to identify current problems related to the organizations business needs. From that, key performance indicators (KPIs) should be established to measure progress. Benchmarking where you are today, looking for real problems and developing measurable KPIs are at the heart of finding and proving the value of AIOps.

For example, de la Parra suggested that organizations could examine their efficiency by tracking the volume of major incidents relative to their applications, or the mean time to detect, acknowledge and resolve incidents. Value could be measured by looking at how much manual work is eliminated, or reductions in the number of issues reported by users.

One participant questioned how long it could take to set up the platform. According to de la Parra, this can be completed within a few weeks in many cases. He recommended starting with a manageable sized pilot to get some meaningful results quickly. Once baseline data is fed into the model, it will start detecting deviations in real-time. In addition, de la Parra noted that the IBM Watson AIOps solution comes with pre-defined algorithms that produce models to accelerate the implementation and the return on investment (ROI). This approach removes the need for data scientists to normalize data, build a data lake, create models, and integrate interfaces to collaborate with the solution such as ChatOps, he said.

Despite the discussion, it was clear that many of the participants remained skeptical about whether AIOps can produce a measurable return on investment. As well, there were questions about the trustworthiness of the data and whether domain-specific tools, such as those that monitor security, are sufficient.

The main advantage of domain agnostic AIOps over domain-specific tools is that it provides complete visibility, said de la Parra. This is what makes it trustworthy AI, he said. Decisions are driven by evidence from analyzing different data sources, grouping entities, localizing issues visualized in topology views to provide context, probable cause and next best action to resolve incidents. This is all done within the confines of policies and compliance requirements.

Its understandable to have skepticism over the effectiveness of AIOps given a common preconception around biased AI in general and the effort to implement solid AI models, said de la Parra. However, when we talk about AIOps at IBM, we are referring to a specific set of capabilities that provide concrete models to support log anomaly detection, blast radius, seasonal event grouping, next best action among others.

Another concern raised by the group related to the issue of false positives on potential incidents. De la Parra noted that AIOps can analyze whether an issue is having an impact on business systems. If there is no impact, it does not send alerts. Reducing the noise is critical to allow staff to spend time on higher value tasks, said de la Parra. A 2021 study from Forrester analyzed the total economic impact of IBM Watson AIOps. It showed a 50 per cent reduction of MTTR (Mean Time to Resolve), 80 per cent time saved from remediating false-positive incidents, leading to $623K in savings and other benefits, such as proactive incident avoidance.

According to de la Parra, AIOps results in better overall IT service management. Not only does it reduce response time and downtime, it can also be used to look at the appropriate resource allocation for workloads in the cloud.

Organizations already have the data, said de la Parra. AIOps enables the IT team to be more proactive and to become a trusted partner that helps drive business forward.

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Does artificial intelligence for IT operations pay off? - IT World Canada

Margie and I were shopping at Sams Wholesale Club yesterday when all of a sudden a floor cleaning machine drove right past us.

This doesnt sound at all eventful, however - at second glance - I could see that the floor cleaning vehicle was driverless. There was a seat, controls, and a steering wheel, but there was no human driver.

As I processed this moment, the first thing I thought about was how cool this is. A driverless, automated vehicle that has a cleaning route all mapped out.

It automatically beeps its horn to alert people of its presence. I watched it break timely for human traffic.

Its amazing, game-changing technology. The store confidently operates this equipment during normal operating hours with people walking right near it. We were there right at 10:00 a.m., yesterday during the opening minutes of operation.

A moment later, I thought, wow, this equipment has taken away a good job that used to exist.

Now, its true that businesses all over America are having a hard time filling numerous open job classifications.

I couldnt help but think about the many jobs that have been eliminated over the past recent years because of technology.

A few years ago,a McKinsey reporthighlighted the following statistics:

Regarding workforce displacement, they conclude that as many as 800 million global jobs and 475 million employees could be disrupted by automation before 2030.

Here are some of the most recent jobs lost due to technology:

Here are six jobs that may disappear by 2030:

Here are five jobs that wont be eliminated by 2030:

In summary, technology is amazing and wonderful. Yet, on the other hand, good jobs that have existed for generations are being eliminated. People will have to become more nimble and adaptable than ever before and be prepared to potentially have to make a career change as the marketplace continues to evolve.

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Artificial Intelligence Is Taking Over Jobs That Humans Did For Years - wpgtalkradio.com

As all agents, brokers, and home buyers know, searching for a home is a deeply personal process, and one of the most difficult challenges for buyers is narrowing down what they want. When a prospective buyer walks through a home or searches for one online, they are making hundreds of value judgments, often without ever consciously realizing them or expressing them to the real estate professional they are working with.

Thankfully, artificial intelligence (AI) can now help bridge that gap and deliver a customized and personalized experience for consumers, without additional work by the agent or broker.

Here are a few exciting ways AI technology is making this possible:

For years, it has been easy to search for homes based on basic criteria like square footage, but what if a client wants something a little more specific, such as hardwood floors in all of the bedrooms, or homes with granite counters and white kitchen cabinets?

Thats where AI comes in. Those kinds of variables, or combinations of them, are not often captured by a listing data feed, but they can be critical to personalizing the customer experience. AI makes it easy to get the right search results quickly for even the most particular clients.

If you watch Netflix or use Amazon, youre already familiar with AI technology that reacts to each individual consumers preferences. On those platforms, what you stop to review, or even the amount of time you spend reviewing, is used to define preferences without ever asking you a specific question. In real estate, AI-powered search platforms are starting to offer buyers similar interactions.

Agents can now encourage consumers to find and upload images of what theyre looking for types of home, the finishes, the features, the layout and have tech tools handle the hard work of searching for similar properties on the market.

Firms like Wayfair, Home Depot, and others are leveraging tools that allow consumers to visualize what a room or a home would look like with different paint colors, with their own furniture or even after a renovation. This allows buyers and sellers to maximize the interest in a transaction by seeing what their home will look like in the future.

Instead of typing something like, New York, three-bedroom apartment, prospects are now able to simply speak into their phone or computer microphone and say something like, I need a three-bedroom apartment with a Central Park view in New York, facing east. And before long, platforms will be able to reply to them verbally. With computer vision technology, that becomes a reality by utilizing plain-English descriptions of what is tagged in images and searching for them.

For sellers, search placement can be improved by using technology that automatically tags home features in listing photos. That means that agents can avoid writing all those tags and detailed image descriptions, but still have their sellers benefit from optimal search engine placement. At a time when the vast majority of home searches start online, thats a big deal.

Put simply, developments like these are increasingly transforming the home search process and making it easy for real estate professionals to deliver an even more highly personalized service for their customers without adding more to their plates.

Red Bell Real Estate, LLC, a homegenius company, is at the forefront of these and other exciting technology developments that will make agents and brokers jobs easier and more lucrative. If youre interested in learning more about how this tech could work for you or your agents, visit homegenius.com.

2021 Radian Group Inc. All Rights Reserved. Red Bell Real Estate, LLC, 7730 South Union Park Avenue, Suite 400, Midvale, UT 84047. Tel: 866-626-2381. Licensed in every State and the District of Columbia. This communication is provided for use by real estate professionals only and is not intended for distribution to consumers or other third parties. This does not constitute an advertisement as defined by Section 1026.2(a)(2) of Regulation Z.

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6 ways artificial intelligence is revolutionizing home search - Inman

Red Hat's Latin America business is growing and the IT firm aims to expand itsproduct portfolio and regional presence in 2022.

The companys regional focuses are financial services, the public sector, and telecommunications. And it is betting on artificial intelligence, with tests on a machine learning and artificial intelligence service already underway. Commercial launch is planned for next year.

In this interview, Red Hats Latin America technology director Thiago Araki also highlights the advance of open source solutions and plans for Central America and the Caribbean.

BNamericas: In which verticals are you seeing the greatest opportunities in Latin America?

Araki: In telecommunications, financial services and government. All of them are going through a moment of transformation.

Operators, with everything to do with the launch of 5G, are making investments to modernize their network. Financial services also, with this movement of fintech, changes in means of payment and open banking. And we see a transformation in government, which seeks to digitize and reduce bureaucracy.

BNamericas: These sectors you mention were reluctant to use open source for security reasons. Has this been resolved?

Araki: Yes, it is finished. I have been at Red Hat for eight years and when I entered, this discussion was taking place, but it is already being perceived that open software is safe. In addition, we have an open source business and offer support so that they can consume this type of technology safely.

Today, acquiring proprietary software and supported open source software is not very different for the company. What does change is that open source offers much more innovation.

BNamericas: How is Latin America doing in terms of open source adoption?

Araki: In general, here you first look at what's working globally and then it's just adopted, but once it's done, the level is comparable to other more mature markets.

BNamericas: And what about the adoption of containerized apps and the Kubernetes automating system?

Araki: That is a good example of what we are saying. These technologies have been in the market for about seven years. We started leading that community in 2014/15. In 2016, we saw the first large companies start adoption.

Today it is really very difficult to find a company that is not using containers and Kubernetes, either in their own datacenters or as a service in the cloud.

Adoption in Latin America is very large, and we have many success stories.

BNamericas: Open RAN and edge computing are emerging with much potential. How does Red Hat fit into these spaces?

Araki: We are among the main investors in open RAN and in edge computing.

We are working on a set of solutions designed for a more distributed model. So, for example, our RHEL OS we made lighter so it can run on the edge. We just released a version of OpenShift that you can now run on a single node, which is also very useful for edge computing.

And, very important are investments in automation tools because all the management of distributed processing is very complex.

BNamericas: What are the investment focuses for next year?

Araki: We are going to continue investing in these issues, but something new we are working on is everything related to machine learning and artificial intelligence. We are about to release a service called OpenShift Science.

We have been investing in open source communities dedicated to the development of artificial intelligence models for some time now, and now we are about to launch this service that aims to be simple so that data scientists or analysts can develop models without the need to be experts. We will continue with very strong investments in the next year.

BNamericas: IBM owns Red Hat and is very strong in AI. How to do plan to compete or complement each other?

Araki: With IBM we sometimes compete and sometimes we collaborate. The main objective of the Red Hat acquisition was to be able to scale what we were already doing and to be able to drive enterprise open source in the hybrid cloud. So, many times we go together combining technologies and in other cases what we do is compete.

Watson [IBM's AI proposition] is an incredible portfolio, but I think there are a lot of companies that want to adopt or are more used to open source. Then we'll be there.

I see a lot of demand for artificial intelligence across industries on credit card fraud detection, security, and, of course, the internet of things (IoT).

In telecommunications, also with the pandemic, we saw great interest in issues of efficient use of networks and their sizing. We have the ability to identify usage patterns and be able to make network configurations in advance.

BNamericas: Is the artificial intelligence portfolio commercially available?

Araki: At the moment it is in the testing phase. Customers can use it at no cost, because the idea is to carry out the tests in the market so that it can then be converted into an offer.

What we also want to do is that, through this service, solutions from companies that are part of the Red Hat ecosystem, such as IBM or startups, can be integrated.

BNamericas: Beyond the product portfolio, what investments is Red Hat making in the region?

Araki: Well, we continue to expand. We are hiring many people; we opened a commercial office in Peru, and despite the uncertainty, we see very important growth.

And this will continue next year. We are also expanding to other markets, where we have a smaller presence now, such as the Caribbean or Central America. There, the demand is increasing a lot.

In addition, we are working with business partners on their training.

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Red Hat bets on artificial intelligence and ... - BNamericas English

Artificial intelligence (AI) powers 5G, blockchain, the internet of things, quantum computing and self-driving cars.

Source: The Scientist Magazine

Artificial intelligencedeals with the area of developing computing systems which are capable of performing tasks that humans are very good at, for example recognising objects, recognising and making sense of speech, and decision making in a constrained environment.

Machine Learningis defined as the field of AI that applies statistical methods to enable computer systems to learn from the data towards an end goal. The term was introduced by Arthur Samuel in 1959.

Neural Networksare biologically inspired networks that extract abstract features from the data in a hierarchical fashion.

Deep Learningrefers to the field of Neural Networks with several hidden layers. Such a Neural Network is often referred to as a Deep Neural Network.

I will refer to AI in this article as covering the spectrum of Machine learning and Deep Learning as well as the classical AI techniques such as Logic and Search algorithms.

Source: Qualcomm

5G refers to "5th Generation", and relates to the newest standards in mobile communications. The performance levels for 5G will be focused on ultra low latency, lower energy consumption, large rates of data, and enormous connectivity of devices. The era of 5G, that will spread around much of the world from 2020 onwards (with some limited deployments in 2019), will be world where cloud servers will continue to be used, and also one whereby we witness the rise in prominence of AI on the edge (on device) where the data is generated enabling real-time (or very near real time) responses from intelligent devices. 5G and edge computing with machine to machine communication will be of great importance for autonomous systems with AI such as self-driving cars, drones, autonomous robots, and intelligent sensors within the context of IoT. 5G with AI will also enable the invisible bank and payments that leading Fintech influencers, such as Brett King and Jim Marous, dream about. The significantly faster speeds of 5G over 4G will enable technologies that are suboptimal today such as Virtual Reality (VR) to perform much better. Augmented Reality (AR) and Holographic technologies will emerge across different use cases in this period too. Those companies that are going to thrive (even survive) the resulting digital transformation will be the ones that are already planning and exploring the potential.

As a society we need to be aware of the impending changes across all sectors of the economy. We need to ensure that our political leaders and regulators actually understand the scale of change that will arise and ensure that the regulatory frameworks and infrastructure are optimised to enable the deployment of AI for improving healthcare with personalized medicine, finance with better services for the customer, marketing with enhanced personalization and better service to the customer, plus smarter and more efficient manufacturing.

The graphic above shows an example of computers on board autonomous cars engaging in Machine to Machine communication as the vehicle in red broadcasts to all other vehicles upon discovering the broken down car.

Every single sector of the economy will be transformed by AI and 5G in the next few years. Autonomous vehicles may result in reduced demand for cars and car parking spaces within towns and cities will be freed up for other usage. It maybe that people will not own a car and rather opt to pay a fee for a car pooling or ride share option whereby an autonomous vehicle will pick them up take them to work or shopping and then rather than have the vehicle remain stationary in a car park, the same vehicle will move onto its next customer journey. The interior of the car will use AR with Holographic technologies to provide an immersive and personalised experience using AI to provide targeted and location-based marketing to support local stores and restaurants. Machine to machine communication will be a reality with computers on board vehicles exchanging braking, speed, location and other relevant road data with each other and techniques such as multi-agent Deep Reinforcement Learning may be used to optimise the decision making by the autonomous vehicles.Deep Reinforcement Learning refers to Deep learning and Reinforcement Learning (RL) being combined together. This area of research has potential applications in finance, healthcare, IoT and autonomous systems such as robotics and has shown promise in solving complicated tasks that require decision making and in the past had been considered as too complex for a machine. Multi-agent reinforcement learning seeks to enable agents that interact with each other the ability to learn collaboratively as they adapt to the behaviour of other agents.Furthermore, object detection using Convolutional Neural Networks (CNNs) will also occur on the edge in cameras too (autonomous systems and also security cameras for intruder detection). ACNN is a type of Deep Neural Network that uses convolutions to extract patterns from the input data in a hierarchical manner. Its mainly used in data that has spatial relationships such as images.

The image above shows an example of Machine to Machine communication between autonomous vehicles and devices that may develop in the world in 5G to enable reduced accidents on the road.

The physical retail sector may transition from one whereby costly inventory is held in bulk to an inventory light model using smart mirrors, AR and VR combined with AI to provide personalised recommendations for apparel. In the event that the customer selects an item then an autonomous vehicle may deliver to the store whilst the customer is enjoying a digital experience and refreshments or to their home at a pre-agreed delivery time. Over time healthcare may evolve into a more efficient sector whereby the next generation of drugs will be developed with personalised medicine in mind so that side effects of a given drug are minimised and the benefits of the medication are maximised and data from Electronic Health Records is mined effectively, and medical imaging with explainable AI deployed efficiently across clinics and hospitals so as to improve timely diagnosis of a condition, and thereby reduce misdiagnosis for patients.

Source: Statista

The chart above illustrates the rapid growth in the number of connected devices. Statista estimated that there will be approximately 31 billion IoT connected devices in 2020 and 75 Billion by 2025. As we move into the world of 5G the role of AI will be of fundamental importance to the economy overall and to your day to day life.

In summaryI believe that AI and the other industry 4.0 digital technologies should be developed and encouraged to drive economic growth in ways that are cleaner, more efficient and allow wider participation across society for education, healthcare and better living standards. The issue of warfare and AI is a highly debated and emotive subject, and automation in warfare has been on display since the first Gulf war in the 1990s with fire and forget and cruise missiles. At the very least it is important to consider the need for transparency with robust frameworks to understand what is being done in order to ensure that there is sufficient oversight as a society over those making the decisions. However, in spite of what some in the media would have us believe, the vast majority of the AI community are not working on developing killer robots nor other autonomous weapons. Whilst attending speaking at an event on AI hosted in Davos during the WEF, I happened to meet Viktoriya Tigipko of TA ventures and@JamesPeyerof@Apollo_Venturesand was impressed with the positive outlook and vision that they had for AI in relation to healthcare and the development of next generation treatments that will help humanity. I have also been inspired by the work of the brilliant Dr Anna Becker who started her degree at the age of 16 and her postgraduate studies at 19 before going on to build and run an AI company. AI and in particular Machine Learning and Deep Learning serve at this moment in time (and in the foreseeable future) to solve for the issue of making sense of the deluge of data that we generate from digital platforms rather than to create Skynet with Terminator machines to wipe us out (AGI itself does not exist today nor the medium term future). AI also provides an opportunity to improve living standards and promote cleaner and more efficient industry, agriculture, smarter cities and energy systems as we move into the world of industry 4.0 with the arrival of 5G.

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How Artificial Intelligence Will Impact Your Daily Life in the 2020s - BBN Times

In a webinar hosted by Keelings Knowledge, Julia Baumanns, Technical Delivery Manager at Microsoft, and Tobias Fausch, CIO at BayWa, talked about future technologies in the fresh produce industry. Baumanns leads a pan-European team working on digital transformation, while Fausch works in sustainable energy at BayWa. The webinar was moderated by Eva Schrder, sales and marketing manager at Keelings Knowledge.

Among the topics discussed was the extent to which the fruit and vegetable industry could benefit from the use of artificial intelligence and process automation, and the extent to which the industry would need to adapt technologically. While the fruit and vegetable industry is already using quite a lot of IT at the moment, it is clearly lagging behind other industries, Eva Schrder said. "The need to move with the times; to go digital is important. Ultimately, the industry also needs to become increasingly digital."

Tobias Fausch from BayWa

Artificial intelligence (AI) is here to stayFausch is convinced that AI will become established. However, he says it will continue to rely on data inserted by humans and is by no means an autonomous intelligence. "AI is already being used to detect defects on products, or unripe fruit," Fausch said. However, he claims that for a farm to become a market leader in this area, not only the industry would have to evolve but AI's themselves.

Baumanns takes a similar view. AI can be a driver for innovation, research and can definitely make a difference competition-wise. In this regard, she mentioned Microsoft's FarmBeats software, a cloud for customers in the fruit and vegetable industry that can collect and process data in real time. "AI, however, is only as good as it is users. If you don't know how to use data, it will not do you much good." At the same time, she said, the use of AI can also provide businesses with new employees, such as data analysts.

Julia Baumanns from Microsoft

"Data is the new oil"Baumanns stresses that understanding data processes in companies is important. Baumanns echoed a well-known mantra in the tech industry, saying: "Data is the new oil." The "customer experience" in particular, but also the "employee experience," can be enhanced by processing data correctly, she said.

Fausch agrees with this, also seeing AI as a way to make certain processes more efficient and streamlined, which in turn saves time and money. "Furthermore, it's also possible to analyze customer participation. It's about improving the customer's experience. It can also be an offer for customers to actively help shape and adjust a company. Through the data a company gains, it can also analyze customer behavior, in order to be able to respond appropriately."

The use of harvesting robots has already created a first step toward automation, he said. Especially in terms of supply chains, there is certainly room for improvement in warehousing/logistics. "At the same time, of course, automation must not increase the company's own CO2 emissions," says Baumanns.

Sustainability and sharing economySustainability and a "sharing economy" are two trends that will remain important topics in the coming years. Sharing economy refers to the idea of previously competing companies cooperating, to combat the effects of climate change. Sustainability means using existing resources efficiently, for example to analyze soil quality.

Fausch emphasizes that the necessity of process automation would have to be measured by how often a particular task is performed, in order for automation to pay off. When there are less than 20,000 repeated tasks per year, there is no need for automation. Process optimization will continue to be a big issue.

Keelings Knowledge is part of the Keelings Group, which was founded by the Keelings family in 1926. The Keelings Group supplies the entire supply chain and focuses on the fresh produce industry. Since 2012 they also provide their software across Europe for external customers such as some production companies, wholesalers, for import and export, and more.

For more information:Eva SchrderKeelings KnowledgeKnigsallee 60 F40212 Dsseldorf Handy.: +49 162 877 5335Tel.: +49 211 89 03 677sales@keelingsknowledge.com https://www.keelingsknowledge.com/de/

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Artificial intelligence and process automation in the fresh produce industry - FreshPlaza.com

C3.ai (NYSE:AI) stock is getting a boost on Monday after ARK Invest CEO Cathie Wood singles out artificial intelligence (AI) as the next big move in investing.

Source: shutterstock

Heres what Wood had to say on the matter at the Milken Institute conference in October, as reported by MoneyWise

We were assuming that in the next 10 years, artificial intelligence would deliver, in the enterprise software space, a market cap opportunity of $30 trillion. Our new number is $80 trillion. We think that is the big new frontier.

With Wood taking such a strong stance on AI, investors are flocking to companies in the space. That includes C3.ai, which has seen some 2.5 million shares change hands as of this writing. Thats just shy of its daily average trading volume of 2.7 million shares.

Now that you know why AI stock is rising today, lets jump into the details about the company.

AI stock was up 3.3% as of Monday morning but is down 59.3% since the start of the year.

Theres more of the most recent stock market news below!

Weve got all the latest stock news for Monday that traders need to know about. That includes the news that has shares of Gores Guggenheim(NASDAQ:GGPI), Akerna(NASDAQ:KERN), and Oatly(NASDAQ:OTLY) on the move. You can find more of this info at the following links!

On the date of publication, William Whitedid not have (either directly or indirectly) any positions in the securities mentioned in this article.The opinions expressed in this article are those of the writer, subject to the InvestorPlace.comPublishing Guidelines.

Article printed from InvestorPlace Media, https://investorplace.com/2021/11/ai-stock-9-things-to-know-about-c3-ai-as-cathie-wood-bets-big-on-artificial-intelligence/.

2021 InvestorPlace Media, LLC

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AI Stock: 9 Things to Know About C3.ai as Cathie Wood Bets Big on Artificial Intelligence - InvestorPlace

List of Top AI Healthcare, Finance, Insurance, Marketing, Manufacturing, Retail & Ecommerce Companies at GoodFirms.

Presently, many businesses seek leading AI companies to help them implement AI technology to gain a competitive benefit within e-commerce, manufacturing, human resources, accounting, customer relations, marketing and many more. Therefore, GoodFirms has unveiled the list of Top AI Companies from various industries like Healthcare, Finance, Insurance, Marketing, Manufacturing, Retail & Ecommerce, and Transportation.

Take a Look at List of Top AI Healthcare, Finance, Insurance, Marketing, Manufacturing, Retail & Ecommerce, and Transportation Companies at GoodFirms:

Top Artificial Intelligence (AI) Companies:

MobiDev, Talentica Software, Sigma Data Systems, SPEC INDIA, Avenga, 7EDGE, SoluLab, Cyber Infrastructure Inc., Redwerk.

https://www.goodfirms.co/artificial-intelligence

Best Healthcare AI Companies:

AiCure, AltexSoft, Apixio, Maxwell Plus, Arterys, Atomwise, CloudMedx, Enlitic, Turbine, Jvion.

https://www.goodfirms.co/artificial-intelligence/healthcare

Best AI Companies in Financial Sector:

Sigmoidal, Kensho, DataVisor, PROWLER.io, Zest AI, Symphony AyasdiAI, Kavout, Alpaca, Vectra, DLabs.

https://www.goodfirms.co/artificial-intelligence/finance

Best AI Companies for Insurance Industry:

H20.ai, Azati Software Corporation, Chisel AI, Gradient AI, Avaamo, daotData, Shift Technology, Fadata, Neutrinos, OSP Labs.

https://www.goodfirms.co/artificial-intelligence/insurance

Best AI Companies for Marketing Industry:

Datorama, Avaus, MindLytiX, GumGum, Albert, NEUON AI, Amplero, Node, BrancoSoft Private Limited, Exemplary Marketing LLC.

https://www.goodfirms.co/artificial-intelligence/marketing

Best AI Companies for Manufacturing:

LeewayHertz, Citadel Analytics, World Wide Technology, 2021.AI, Uptake, Quantellix ML, Wizata, Hacarus, Emerton Data, Augmentir.

https://www.goodfirms.co/artificial-intelligence/manufacturing

Best Retail & Ecommerce AI Companies:

Redwerk, AltexSoft, Peak, Rsystems, Datamatics, Digifutura Technologies, ThoughtSpot, Unicsoft, Chop Dawg, Hey Machine Learning.

https://www.goodfirms.co/artificial-intelligence/retail-ecommerce

Best AI Companies In Transportation:

Trigent, Endive Software, TechSpeed, Space-O Technologies, Django Stars, IntelliCompute, Prakash Software Solutions Pvt. Ltd., Celadon, PerfectionGeeks Technologies, TechnoYuga Pvt. Ltd.

https://www.goodfirms.co/artificial-intelligence/transportation

Internationally recognized GoodFirms is a maverick B2B research, ratings, and reviews platform. It builds a bridge for the service seekers to associate with the most excellent partners. The research team of GoodFirms evaluates each firm through several qualitative and quantitative measures.

The research mainly includes three main factors that are Quality, Reliability, and Ability. Further, these components are subdivided into numerous metrics, such as verifying the past and present portfolio of each agency, years of experience in the expertise area, online market penetration, and reviews from clients.

Focusing on overall research, every agency is assessed and provided with a set of scores that are out of a total of 60. Hence, according to these points, all the firms are indexed in the list of top development companies, most excellent software, and varied sectors of industries.

Moreover, GoodFirms supports the service providers by asking them to engage in the research process and show evidence of their work. Thus, grab an opportunity to Get Listedfor free in the list of top companies as per the categories. Obtaining the position at GoodFirms among the best service providers helps firms to expand their reach to new prospects globally, increase their productivity and sales

About GoodFirms:

GoodFirms is a Washington, D.C. based research firm that aligns its efforts in identifying the most prominent and efficient Artificial Intelligence (AI) companies that deliver results to their clients. GoodFirms research is a confluence of new age consumer reference processes and conventional industry-wide review & rankings that help service seekers leap further and multiply their industry-wide value and credibility.

Rachael Ray(360) 326-2243 [emailprotected]

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GoodFirms Announces the List of Top Artificial Intelligence (AI) Companies Globally for Varied Industries - 2021 - PRNewswire

Bosch AI or the Bosch Center for Artificial Intelligence is focused on deploying advanced technologies such as AI across a wide range of Bosch products and services. AI strategies of Bosch are implemented in more than 185 projects in seven locations such as India, the US, Israel, Germany, and China. Bosch AI collects real-time data from different business departments and conducts AI research on safe, reliable, and explainable AI. Lets explore how Bosch leverages AI in different ways for meeting client satisfaction.

Bosch AI is popular for applying big data and machine learning to different varieties of Bosch products and services for accurate AI solutions. It helps to increase efficiency, optimize the supply chain, and enhance quality while reducing costs. The deep learning techniques are one of the AI strategies of Bosch to implement multiple smart functionalities such as automated optical inspection, anomaly detection, root cause analysis, production scheduling, and many more.

Bosch AI leverage artificial intelligence to extract value from the available real-time data to improve functionalities and perform efficiently and effectively. AI can streamline and improve technical capabilities to explore innovative approaches in this global tech-driven market. Supply chain management can utilize the power of Bosch AI for effective inventory management, demand forecasting, optimizing packaging sizes, and ensuring the availability of high-quality products and services.

Bosch has brought a transformation in the tech industry with the combination of state-of-the-art artificial intelligence technologies and domain expertise. Bosch AI is determined to recruit employees who are qualified professionals and graduates with sufficient experience in AI to employ the best in the field of artificial intelligence.

One of the AI strategies of Bosch AI is to create differentiating artificial intelligence solutions with concrete lead applications like AI-based dynamics modeling like Gaussian process-based models, control optimization through reinforcement learning, and large-scale deep learning. The research field of Bosch AI is focused on learning AI-based models with optimal data acquisition without destroying the system and investigating multiple machine learning approaches to control the system while guaranteeing controller stability.

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Bosch AI: Artificial Intelligence Technologies at the Heart of Bosch Products - Analytics Insight