Phoenix, Arizona--(Newsfile Corp. - February 22, 2021) - The Stock Day Podcast welcomed Artificial Intelligence Technology Solutions (OTC Pink: AITX) ("the Company"), an innovator in the delivery of artificial intelligence-based solutions that empower organizations to gain new insight, solve complex challenges and fuel new business ideas. CEO of the Company, Steve Reinharz, joined Stock Day host Everett Jolly.

Jolly began the interview by asking about the Company's background and current projects. "It was 2017 when I did the reverse merger into the public company that I then renamed 'Artificial Intelligence Technology Solutions'," explained Reinharz, after elaborating on his experience in the artificial intelligence space.

"What are your revenue goals moving forward and what markets are you hoping to penetrate?" asked Jolly. "As far as revenues go, we've had double-digit percent growth month-over-month, which is fantastic. That kind of exponential growth is going to be one of our goals for the foreseeable future," said Reinharz, noting that the Company has also been working to improve the Company's financials. "We had a situation where sales were outpacing our ability to produce, so we moved into a new assembly location in California, and recently it looks like we have been outgrowing that facility, which will accelerate our plans to move into a much larger facility in Michigan presumably before the end of this year."

"From a sales growth standpoint, I couldn't be happier. From a market acceptance standpoint, we've got a long way to go. We're just scratching the surface," said Reinharz. "We have a platform-style solution that applies across a variety of markets," he continued. "We have five products in the market right now," said Reinharz. "We have a very focused road map for 2021 and 2022 from a development standpoint. So, we're just going to continue to build and grow," he shared. "Our mission is to be the incumbent in this new space that we are creating, which we call the 'Autonomous Remote Services' space."

Story continues

"For the Robotic Assistance Devices (RAD), what is the price point from the lowest to the highest?" asked Jolly. "It is going to be from about $700 to $6,000 per month," explained Reinharz, noting that the Company's solutions drastically reduce the cost of traditional manned security services.

The conversation then turned to a recent advertisement released by the Company. "We wanted to participate in a positive messaging piece," said Reinharz. "It's called 'America Renewed' on USA TODAY," he added. "We wanted to be a part of the story of how we are protecting ourselves and staying healthy using technology and autonomous systems to do it," explained Reinharz. "We had paid participation by all of the dealers on the ad," he shared, noting that the advertisement represented both a low-cost and high-visibility opportunity for the Company.

To close the interview, Reinharz encouraged listeners to consider the future and potential of the artificial intelligence space as workplace roles continue to evolve and embrace technology. Reinharz also expressed his gratitude for the opportunity to reach listeners and shareholders, and encouraged listeners to take a closer look at the Company's current projects and new developments.

To hear the entire interview with Steve Reinharz, follow the link to the podcast here: https://audioboom.com/posts/7805851-artificial-intelligence-technology-solutions-discusses-the-potential-of-its-robotic-assistance-de

Investors Hangout is a proud sponsor of "Stock Day," and Stock Day Media encourages listeners to visit the company's message board at https://investorshangout.com/

CAUTIONARY DISCLOSURE ABOUT FORWARD-LOOKING STATEMENTS

This release contains "forward-looking statements" within the meaning of Section 27A of the Securities Act of 1933, as amended, and Section 21E the Securities Exchange Act of 1934, as amended and such forward-looking statements are made pursuant to the safe harbor provisions of the Private Securities Litigation Reform Act of 1995. Statements in this news release other than statements of historical fact are "forward-looking statements" that are based on current expectations and assumptions. Forward-looking statements involve risks and uncertainties that could cause actual results to differ materially from those expressed or implied by the statements, including, but not limited to, the following: the ability of Artificial Intelligence Technology Solutions to provide for its obligations, to provide working capital needs from operating revenues, to obtain additional financing needed for any future acquisitions, to meet competitive challenges and technological changes, and other risks. Artificial Intelligence Technology Solutions undertakes no duty to update any forward-looking statement(s) and/or to confirm the statement(s) to actual results or changes in Artificial Intelligence Technology Solutions expectations.

About Artificial Intelligence Technology Solutions (AITX)

AITX is an innovator in the delivery of artificial intelligence-based solutions that empower organizations to gain new insight, solve complex challenges and fuel new business ideas. Through its next-generation robotic product offerings, AITX's RAD and RAD-M companies help organizations streamline operations, increase ROI and strengthen business. AITX technology improves the simplicity and economics of patrolling and guard services, and allows experienced personnel to focus on more strategic tasks. Customers augment the capabilities of existing staffs and gain higher levels of situational awareness, all at drastically reduced cost. AITX solutions are well suited for use in multiple industries such as enterprises, government, transportation, critical infrastructure, education and healthcare. To learn more, visit http://www.aitx.ai and http://www.roboticassistancedevices.com, or follow Steve Reinharz on Twitter @SteveReinharz.

ContactsInvestor Relations Contact The Waypoint Refinery, LLC(845) 397-2956www.thewaypointrefinery.com

Steve Reinharz (949) 636-7060

About The "Stock Day" Podcast

Founded in 2013, Stock Day is the fastest growing media outlet for Nano-Cap and Micro-Cap companies. It educates investors while simultaneously working with penny stock and OTC companies, providing transparency and clarification of under-valued, under-sold Micro-Cap stocks of the market. Stock Day provides companies with customized solutions to their news distribution in both national and international media outlets. The Stock Day Podcast is the number one radio show of its kind in America.

SOURCE:

Stock Day Media

(602) 821-1102

To view the source version of this press release, please visit https://www.newsfilecorp.com/release/75058

The rest is here:
Artificial Intelligence Technology Solutions Discusses the Potential of Its Robotic Assistance Devices (RAD) with The Stock Day Podcast - Yahoo...

People put the intelligence in artificial intelligence

Is the digital workforce ready to take over? Well, not quite. Artificial intelligence may be capable of assuming many tasks, but it will be some time, if ever, that it could replace jobs on a widespread basis. It simply has too many limitations.

Instead, we need to acquaint a generation of workers with technologies to take on the more mundane, repetitive portions of their jobs, and in turn elevate their decision-making roles within enterprises. Thats the word from Steve Shwartz,AI author, researcher and investor, who points out that the notion of AI taking jobs is a myth. However, AI will have a profound impact on employment.

Shwartz, author of the just-published book Evil Robots, Killer Computers, and Other Myths: The Truth About AI and the Future of Humanity, points out that many people are concerned that intelligent robots will be able to read manuals, take courses, and eliminate all our jobs. Fortunately, this is science fiction.

Todays AI systems are only capable of learning functions that relate a set of inputs to a set of outputs, he says. This simple paradigm has enabled fantastic technological accomplishments such as facial recognition, language translation, and cars that can see and avoid pedestrians. However, these learned functions have no more intelligence than a function that translates Fahrenheit temperatures to Celsius temperatures.

It would take a huge breakthrough to create intelligent robots, and todays AI researchers have only vague ideas about how to create such a breakthrough, Shwartz says. Such a breakthrough is about as likely as time travel.

The bottom line is that any job that requires commonsense reasoning is safe; probably for our lifetimes. Maybe forever, he continues. People-oriented skills in finance, marketing, sales, and HR are probably safe. The types of jobs that will be impacted and not necessarily negative impacted are ones that involve repetitive decision-making that can be learned by AI systems.

Rather than replace jobs, AI is replacing tasks especially repetitive, data-oriented analyses are candidates for automation by AI systems. If it is possible to create a large training set of examples in which each example is labeled with the correct answer, that analysis can likely be learned by an AI system, says Shwartz.

Another task category that AI will enhance is repetitive customer service interactions, he continues. AI-based chatbots are assuming more customer-service work, and customer service jobs that involve a human following a script to interact with customers are at the most risk. Human interactions that require real, unscripted conversations are not at risk.

For non-technical careers, the greatest impact is the availability of massive amounts of data, Shwartz says. The field of marketing has already been transformed by data. Marketers analyze data from Google to determine which keywords to buy. They analyze huge amounts of customer data to determine which campaigns should be targeted to which customers. And they analyze massive databases of web traffic to determine what changes to make to their websites. Todays marketers need to be data analysts. Most companies are relying more and more on data to drive the business. Many formerly non-technical jobs now require extensive data analysis. Workers who do not adapt will be left behind.

While AI will be replacing many repetitive tasks and amplifying intelligence through data, the most exciting opportunities will be seen with the creation of new types of businesses. Shwartz was a founder of one of the first AI companies, Cognitive Systems, in 1986. As an angel investor, Shwartz now sees large numbers of startups whose business models are only possible because of AI technology: Computer-vision technology enables computers and robots to identify objects, faces, and activities. Startups are developing in-store products that identify customers and provide highly personalized offers direct to their smartphones. Companies are developing surveillance products for law enforcement and the military. Startups are creating AI-based medical applications to read MRIs and diagnose diseases. Other vendors are using other types of AI technology to detect fraud and stop cyber-attacks, analyze legal documents, predict the weather, improve search results, and even design golf clubs.

Along with achieving greater sophistication and better mimicking human reasoning, AI also brings additional challenges, Shwartz relates. Computer-vision systems have been shown to be biased against minorities. It is not only unethical for companies to roll out biased systems, but also bad for business. In Europe, due to GDPR regulations, it is illegal and similar regulations are almost certain to follow in the US. These biases are often created inadvertently using biased data. Ensuring systems are non-discriminatory can be harder than developing the technology in the first place.

Read more:
Needed: People To Put The Intelligence In Artificial Intelligence - Forbes

MOL's 300,000 dwt Brasil Maru bulk carrier (Mitsui O.S. K. Lines)

By The Maritime Executive 02-10-2021 05:39:48

Efforts are progressing to harness emerging technologies to improve the efficiency of shipping operations. Japans Mitsui O.S.K. Lines announced that it is expanding its efforts with artificial intelligence to achieve greater efficiency with routing which will also contribute to lower emissions from their ships.

Building on a partnership that began in 2019, MOL is working with Bearing, a Silicon Valley-based AI technology startup, to improve efficiencies within the maritime industry. Together the two companies are developing a range of products that combine MOL's maritime expertise and Bearing's AI technology infrastructure.

Bearing, according to MOL, is building technologies using highly-accurate ship performance models built off of a diverse set of real-world data points. These AI-powered models with some historical voyage data for certain vessels such as vessel speed, trim, main engine operation, weather, and sea condition allow Bearing to predict metrics like fuel consumption with state-of-the-art accuracy even without vessels' design parameters.

Through various trials and intensive discussions concerning ship modeling, MOL announced that it has developed an AI-powered Smart Routing Engine. This application automatically analyzes multiple potential routes for a given voyage and recommends prudent, efficient routing through the use of optimal main engine output and propeller RPM profiles.

MOL says that it continuously monitors the condition of its fleet to ensure optimum operational efficiency which is being further aided by combining the technologies of Bearing as well as other existing and new solutions. Through the addition of AI technology to the existing voyage routing systems, MOL expects that it will be able to further enhance the operations of its fleet which currently numbers approximately 800 ships in operation.

MOL says that it understands the transformative potential of AI and looks forward to leveraging Bearings AI expertise and background in building scalable AI technology products to further advance operating efficiencies.

Follow this link:
Artificial Intelligence to Improve the Shipping Industry's Efficiency - The Maritime Executive

Unfortunately, Commercial AI is Failing. Heres Why.

What happens when a product fails to justify its hype? It fails. In the 90s, interest was booming and many companies wanted to take advantage, but could not. Only a few survived, and when the dot-com bust happened, those companies had to shut down due to lack of effort. In the mid-2000s, cloud computing was the hot topic. Several companies tried to gain momentum but failed because they could not move their data to the cloud.

Commercial AI products are not booming as expected. This is leading to disappointments not only to the artificial intelligence developers but also to the industry and businesses who wanted to employ these products. This is also known as AI fatigue, when a product is unable to deliver the results as promised by its hype, informational, and sometimes, misinformation. For example, when companies were developing a chatbot for Facebooks Messenger, they observed a 70% failure rate in handling user requests. According to a research report by McKinsey Global Institute, 45% of work activities can be automated, of which 80% is enabled by machine learning. Companies in sectors like manufacturing and health care have captured less than 30% of the potential from their data.

One of the reasons why AI products fail to make an impact on a commercial scale is the lack of deep learning. Deep Learning is a subset of AI. Most often, it is used to classify data problems that involve finding data patterns. But many in the AI industry have found it challenging to build artificial intelligence products with deep learning. This issue can be tackled by producing scalable AI products.

If an AI is both accurate and powerful, it is known as scalable AI. In this context, powerful refers to AIs ability to adapt itself to any business model. For example, a medical imaging AI should work in different clinical settings and for patients worldwide. Silicon Valley investor Andreessen Horowitz, who worked with a range of AI companies wrote in his recent article about the lack of scalable AI. It is becoming a challenge in the AI industry to make a program scalable for commercial use, once its out of the lab. If we look deeper, the problem is not with AI, though. The problem lies in the way these AI applications are made for commercial use. On paper, it has a different perspective than when it is put to use.

To build scalable AI for commercial use, the industry needs to shift its focus from data quantity and AI accuracy to data quality, diversity, powerfulness, and knowledge about the industry to fix the problem.

Scalable AI cannot work with poor quality data. It affects both the accuracy and powerfulness of AI. Even a 1% error in data can impact AI accuracy. As a practice, AI practitioners must clean the data. Having effective data cleaning methods to improve data quality is a significant factor to build a robust scalable AI.

A globally diverse dataset is essential for testing and validating the AI. As scalable AI should be robust and powerful for it to function universally, the need for additional investment and efforts to take a global approach has arisen. In healthcare, AI can be biased to a certain section of people or clinics. Healthcare problems are global, hence it is crucial to take this approach. But collecting global data is complicated. The easiest way for AI companies is to collect data from one or more clinics to have a large dataset, preferably from a prestigious clinic that has larger patient data.

Why is scalable AI important? Because people have started doubting the credibility of AI in the commercial space. An industry transformation is needed for companies to believe in AI and employ them. This means a big change in the companys organizational DNA. AI companies that will succeed in making commercial AI will be the ones that focus on creating scalable AI for business learning and business functions. This will harness the transformative power of AI.

Share This ArticleDo the sharing thingy

Go here to read the rest:
Unfortunately, Commercial AI is Failing. Here's Why. - Analytics Insight

Stockholm, Sweden, Feb. 10, 2021 (GLOBE NEWSWIRE) -- Logical Clocks announces three new research projects part of the European Union (EU) Horizon 2020 research and innovation programme that will benefit from Hopsworks artificial intelligence (AI) capabilities to scale deep learning and enhance research focused on understanding environmental changes and improving healthcare in Europe. Hopsworks is the worlds first and most advanced managed Feature Store with an end-to-end AI platform for the development and operation of AI applications at scale.

The European Union leads the world when it comes to leveraging AI for the benefit of the environment and public health comments Jim Dowling, CEO at Logical Clocks and Associate Professor at KTH Royal Institute of Technology in Sweden. Research is part of our DNA and we are proud to be one of the few AI companies leading projects that will ultimately enhance quality of life, not just in the European Union, but around the world.

AI Applications to Improve HealthcareThe Human Exposome Assessment Platform (HEAP) is building a research platform, leveraging AI capabilities, to reveal the influence of environmental factors on human health, such as the link between airborne particles and predisposition to late-onset disease such as cancer. The project received 11 million funding from the European Union for 11 partners from 6 European countries to combine machine learning with computational statistics and develop powerful statistical modelling tools. With Hopsworks metadata mechanisms, which makes large volumes of data easily searchable, accessible and shareable, the HEAP platform will not only unlock new insights but it will also facilitate sharing data in a secure environment, becoming an open resource for the research communities as well as policy-makers across the world.

AI Applications to Predict Climate ChangesThe DeepCube project tackles, through AI, urgent problems caused by climate change in Europe and the whole Mediterranean region, such as forecasting of localized extreme drought and deadly heat impacts in Africa. The project is part of a consortium formed by 9 organizations from 6 European countries that will combine cutting-edge technologies, such as the Hopsworks platform for machine learning, the Earth System Data Cube, and an advanced visualization tool, to extract meaningful information from a large volume of data and to develop data-driven AI models. Funded with 4 million million by the European Union, the project will develop AI applications by extracting extract data from the Copernicus Earth Observation programme which already produces annually more than 3 petabytes of free, open and high quality data from satellites and from non-conventional data sources, such as social network data, industry-specific data, and sensor data.

AI Applications to Improve Food Security and Navigation SafetyThe ExtremeEarth project focuses on the most concerning issues of food security, such as water availability for irrigation of vegetation growth for the former. Currently 20 percent of the agricultural areas of the world are irrigated, producing 40 percent of the global food. The project is also dedicated to developing near real-time automated sea mapping, positively impacting the maritime sea navigation and safety, thus improving the life of 4 million people living in the Arctic. Currently, sea ice information is available either as ice charts or as satellite data, a practice that requires time consuming expert analysis to produce and, consequently, leads to less frequent updates than desired. With support of 11 organizations, the project is implementing state-of-the-art technologies such as Hopeworks Deep Learning and big data processing of massive amounts of data. ExtremeEarth received 6 million funding from the European Union and it is generating key insights for the development of sustainable practices with high significant financial impacts.

The Hopsworks platform will play a major role in going beyond the current state of the state-of-the-art of AI technologies, especially when addressing large volumes of data and scale-out deep learning, while remaining open source. We will continue to make Hopsworks available for free to researchers across the world to bring answers to problems that concern all of us, comments Dowling.

About Logical ClocksLogical Clocks was founded by the team that created and continues to drive Hopsworks Feature Store, the worlds first and most complete feature store with an end-to-end machine learning platform. With offices in Stockholm, London and Palo Alto, Logical Clocks aims to simplify the process of refining data into intelligence at scale.

Visit link:
Tech for Good: Artificial Intelligence Applications that will Improve the Environment and Healthcare in the EU - GlobeNewswire