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Finally, the goal of any healthcare organization is to provide the best possible care to patients. Predictive AI can contribute significantly to this goal by enabling more accurate diagnoses, tailored treatment plans and earlier interventions.

From the patient’s perspective, this translates to better health outcomes, reduced hospital stays and increased satisfaction with their care. For healthcare organizations, improved patient experiences lead to higher patient retention rates, positive word-of-mouth referrals and better performance on patient satisfaction metrics, which are increasingly tied to reimbursement rates in many healthcare systems.

As we’ve explored, the benefits of predictive AI extend far beyond improved diagnostics and treatment plans. It’s a catalyst for operational excellence, financial optimization, availability of investments and long-term growth. From resource management to building an authoritative brand, predictive AI touches every aspect of the healthcare business environment.

Innovative Diagnostic Solutions To Enhance Patient Experiences And Health Provider Decisions — Dr. Deborah Sesok-Pizzini, MD, MBA — Chief Medical Officer & Senior Vice President, Labcorp Diagnostics; Global Head of Quality And Discipline Director, Immunohematology.

Dr. Deborah Sesok-Pizzini, MD, MBA, is Chief Medical Officer And Senior Vice President, Labcorp Diagnostics, and Global Head of Quality And Discipline Director, Immunohematology, Labcorp (https://www.labcorp.com/deborah-sesok…), where she is involved in furthering the company’s initiatives to enhance the patient experience, enable health provider decisions and develop innovative testing solutions.

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A New Zealand-based startup has developed a method of safely and wirelessly transmitting electric power across long distances without the use of copper wire, and is working on implementing it with the country’s second-largest power distributor.

The dream of wireless power transmission is far from new; everyone’s favorite electrical genius Nikola Tesla once proved he could power light bulbs from more than two miles away with a 140-foot Tesla coil in the 1890s – never mind that in doing so he burned out the dynamo at the local powerplant and plunged the entire town of Colorado Springs into blackout.

Tesla’s dream was to place enormous towers all over the world that could transmit power wirelessly to any point on the globe, powering homes, businesses, industries and even giant electric ships on the ocean. Investor J.P. Morgan famously killed the idea with a single question: “where can I put the meter?”

One of the most distinctive features of the Transhumanist project is its unflagging confidence that the advances of science and technology will usher humanity into a marvellous post-human future.

No one has expressed this more sharply than Ray Kurzweil, the futurist and engineering director at Google. In his book, The Singularity is Near (2005), Kurzweil famously writes:

… A future period during which the pace of technological change will be so rapid, its impact so deep, that human life will be irreversibly transformed. Although neither utopian or dystopian, this epoch will transform the concepts that we rely on to give meaning to our lives, from our business models to the cycle of human life, including death itself.

Despite these challenges, the potential rewards of edge AI are driving innovation in model optimization, device management and security solutions. As these advancements continue, the barriers to edge AI deployment are gradually being lowered, paving the way for its widespread adoption across industries.

Ultimately, edge computing democratizes AI by removing it from complex, costly cloud execution and moving it to the local, accessible devices companies already own and use. This means that small and medium-sized businesses can gain access to tools previously reserved for much larger companies.

As we move forward, AI in business and edge computing are intertwined. The ebb and flow of progress is already noticeable in edge computing applications, and AI will continue this trajectory. As edge devices become more powerful, the proliferation of intelligent applications that operate seamlessly at the edge will transform industries.

Could food delivery robots with zero carbon emissions influence a customer’s decision to buy food using them instead of robot vehicles that emit carbon into the atmosphere? This is what a recent study published in the International Journal of Hospitality Management hopes to address as a tea of researchers from Washington State University (WSU) investigated how a customer’s knowledge of an automatic delivery robot’s (ADR) environment impact influences their choice regarding which type of robot they want delivering their food. This study holds the potential to help scientists, environmental conservationists, and the public better understand the benefits of eco-friendly delivery robots for both the short and long term.

“Much of the marketing focus has been on the functionality and the convenience of these automatic delivery robots, which is really important, but it would enhance these efforts to promote their green aspects as well,” said Jennifer Han, who is a doctoral student in WSU’s Carson College of Business and lead author of the study.

For the study, the researchers used the Amazon crowdsourcing platform, MTurk, to conduct an online survey comprised of 418 adults who were instructed to watch videos about ADRs followed by a questionnaire regarding the environmental impact and the risk of using ADRs for their food delivery service. In the end, the team discovered a connection between participants who found ADRs were less risky and wanted an eco-friendly ADR compared to participants who thought ADRs were riskier but weren’t concerned about the environmental consequences.

While generative AI tools have been heralded as the future of education, more than 40 years of academic research suggests that it could also harm learning in realms from online tutoring to employee training for three reasons. First, the best student-teacher relationships are empathetic ones but it is biologically impossible for humans and AI to develop mutual empathy. Second, AI might help us bypass the boring task of knowledge accumulation but it is only through that process that we develop higher order thinking skills. Finally, digital tools are notoriously distracting and multitasking diminishes learning. As we think about the benefits of new technology, we must also consider the risks.

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Three fundamental problems with using LLMs as teachers, tutors, and trainers.

With inventions like a nanomaterial-based battery for IoT and nanoscale transistors, the future of nanotechnology in this field seems to have potential. For now, any large-scale applications are likely years away. Companies must overcome technical, cost, and implementation hurdles before progressing to mass-market applications.

However, numerous nanoscale-sized discoveries and inventions will likely emerge in the coming years. As the value of nanotechnologies and IoT continue increasing, more investors, business owners, and researchers will explore possible use cases. While their inventions may not hit shelves for years, their development speed will surely accelerate.

Data centers are facilities that house the computing hardware used to process and store data. While some businesses maintain their own data centers on site, many others rely on ones owned and operated by someone else.

As our digital world continues to grow, demand for data centers — and clean electricity to operate them — is also increasing. To find out how we’ll be able to keep up, let’s look at the history of data centers, the challenges facing them, and ideas for overcoming those issues — on land, at sea, and in space.