Lifeboat Foundation: Safeguarding Humanity
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Category: information science – Page 173

Smart factories will be very useful in metaverse.workers can operated machines in factories using Internet.

As the idea of interconnected and intelligent manufacturing is gaining ground, competing in the world of Industry 4.0 can be challenging if you’re not on the very cusp of innovation.

Seeing the growing economic impact of IIoT around the globe, many professionals and investors have been asking themselves if the industry is on the verge of a technological revolution. But judging from the numbers and predictions, there is tangible and concrete evidence that the idea of smart manufacturing has already burst into corporate consciousness. According to IDC, global spending on the Internet of Things in 2020 is projected to top $840 billion if it maintains the 12.6% year-over-year compound annual growth rate. There is no doubt that a huge part of this expenditure will be devoted to the introduction of IoT into all types of industry, especially including manufacturing.

But there is not only the forecasts and statistics to tell us that the idea of Industrial Internet of Things is gaining traction across virtually all business sectors. Having already proven to be the crunch point in manufacturing, IIoT brings the reliability of the machine to machine communication, the security of preventive maintenance and the insight of big data analytics. In other words, the IIoT revolution has already begun.

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The first-generation AI systems did not address these needs, which led to a low adoption rate. But the second-generation AI systems are focused on a single subject – improving patients’ clinical outcomes. The digital pills combine a personalised second-generation AI system along with the branded or generic drug and improve the patient response as it increases adherence and overcomes the loss of response to chronic medications. It works on improving the effectiveness of drugs and therefore reducing healthcare costs and increasing end-user adoption.

There are many examples to prove that there is a partial or complete loss of response to chronic medications. Cancer drug resistance is a major obstacle for the treatment of multiple malignancies, one-third of epileptics develop resistance to anti-epileptic drugs; also, a similar percentage of patients with depression develop resistance to anti-depressants. Other than the loss of response to chronic medications, low adherence is also a common problem for many NCDs. A little less than 50% of severely asthmatic patients adhere to inhaled treatments, while 40% of hypertensive patients show non-adherence.

The second-generation systems are aimed at improving outcomes and reducing side effects. To overcome the hurdle of biases induced by big data, these systems implement an n = 1 concept in a personalised therapeutic regimen. This focus of the algorithm improves the clinically meaningful outcome for an individual subject. The personalised closed-loop system used by the second-generation system is designed to improve the end-organ function and overcome tolerance and loss of effectiveness.

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Artificial intelligence drug design company Iktos, and South Korean clinical research biotech Astrogen announced today a collaboration with the goal of discovering small-molecule pre-clinical drug candidates for a specific, undisclosed, marker of Parkinson’s disease (PD).

Under the terms of the agreement, whose value was not disclosed, Iktos will apply its generative learning algorithms which seek to identify new molecular structures with the potential address the target in PD. Astrogen, which has a focus of the development of therapeutics for “intractable neurological diseases,” will provide in-vitro and in-vivo screening of lead compounds and pre-clinical compounds. While both companies will contribute to the identification of new small-molecule candidates, Astrgoen will lead the drug development process from the pre-clinical stages.

“Our objective is to expedite drug discovery and achieve time and cost efficiencies for our global collaborators by using Iktos’s proprietary AI platform and know-how,” noted Yann Gaston-Mathé, president and CEO of Paris-based Iktos in a press release. “We are confident that together we will be able to identify promising novel chemical matter for the treatment of intractable neurological diseases. Our strategy has always been to tackle challenging problems alongside our collaborators where we can demonstrate value generation for new and on-going drug discovery projects.”

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The research study of Spanish clinical neuropsychologist Gabriel G. De la Torre, Does artificial intelligence dream of non-terrestrial techno-signatures?, suggests that one of the “potential applications of artificial intelligence is not only to assist in big data analysis but to help to discern possible artificiality or oddities in patterns of either radio signals, megastructures or techno-signatures in general.”

“Our form of life and intelligence,” observed Silvano P. Colombano at NASA’s Ames Research Center who was not involved in the study, “may just be a tiny first step in a continuing evolution that may well produce forms of intelligence that are far superior to ours and no longer based on carbon ” machinery.”

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Artificial intelligence is unlike previous technology innovations in one crucial way: it’s not simply another platform to be deployed, but a fundamental shift in the way data is used. As such, it requires a substantial rethinking as to the way the enterprise collects, processes, and ultimately deploys data to achieve business and operational objectives.

So while it may be tempting to push AI into legacy environments as quickly as possible, a wiser course of action would be to adopt a more careful, thoughtful approach. One thing to keep in mind is that AI is only as good as the data it can access, so shoring up both infrastructure and data management and preparation processes will play a substantial role in the success or failure of future AI-driven initiatives.

According to Open Data Science, the need to foster vast amounts of high-quality data is paramount for AI to deliver successful outcomes. In order to deliver valuable insights and enable intelligent algorithms to continuously learn, AI must connect with the right data from the start. Not only should organizations develop sources of high-quality data before investing in AI, but they should also reorient their entire cultures so that everyone from data scientists to line-of-business knowledge workers understand the data needs of AI and how results can be influenced by the type and quality of data being fed into the system.

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We see that text data is ubiquitous in nature. There is a lot of text present in different forms such as posts, books, articles, and blogs. What is more interesting is the fact that there is a subset of Artificial Intelligence called Natural Language Processing (NLP) that would convert text into a form that could be used for machine learning. I know that sounds a lot but getting to know the details and the proper implementation of machine learning algorithms could ensure that one learns the important tools in the process.

Since the r e are newer and better libraries being created to be used for machine learning purposes, it would make sense to learn some of the state-of-the-art tools that could be used for predictions. I’ve recently come across a challenge on Kaggle about predicting the difficulty of the text.

The output variable, the difficulty of the text, is converted into a form that is continuous in nature. This makes the target variable continuous. Therefore, various regression techniques must be used for predicting the difficulty of the text. Since the text is ubiquitous in nature, applying the right processing mechanisms and predictions would be really valuable, especially for companies that receive feedback and reviews in the form of text.

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An echo chamber is an infinity of mirrors. Photo: Robert Brook via Getty Images

“One way the internet distorts our picture of ourselves is by feeding the human tendency to overestimate our knowledge of how the world works,” writes philosophy professor Michael Patrick Lynch, author of the book The Internet of Us: Knowing More and Understanding Less in the Age of Big Data, in The Chronicle of Higher Education. “The Internet of Us becomes one big reinforcement mechanism, getting us all the information we are already biased to believe, and encouraging us to regard those in other bubbles as misinformed miscreants. We know it all—the internet tells us so.”

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In the 2002 science fiction blockbuster film “Minority Report,” Tom Cruise’s character John Anderton uses his hands, sheathed in special gloves, to interface with his wall-sized transparent computer screen. The computer recognizes his gestures to enlarge, zoom in, and swipe away. Although this futuristic vision for computer-human interaction is now 20 years old, today’s humans still interface with computers by using a mouse, keyboard, remote control, or small touch screen. However, much effort has been devoted by researchers to unlock more natural forms of communication without requiring contact between the user and the device. Voice commands are a prominent example that have found their way into modern smartphones and virtual assistants, letting us interact and control devices through speech.

Hand gestures constitute another important mode of human communication that could be adopted for human-computer interactions. Recent progress in camera systems, image analysis and machine learning have made optical-based gesture recognition a more attractive option in most contexts than approaches relying on wearable sensors or data gloves, as used by Anderton in “Minority Report.” However, current methods are hindered by a variety of limitations, including high computational complexity, low speed, poor accuracy, or a low number of recognizable gestures. To tackle these issues, a team led by Zhiyi Yu of Sun Yat-sen University, China, recently developed a new hand gesture recognition algorithm that strikes a good balance between complexity, accuracy, and applicability.

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