The Department of Homeland Security has awarded funding worth $1 million each to Alakai Defense Systems and Physical Sciences Inc. to further develop their machine learning platforms to help improve the detection of explosives, narcotics, chemical agents and other threats as part of the second phase of the Small Business Innovation Research program.

“Our impetus for developing these machine-learning modules stems from the Transportation Security Administration’s operational needs for threat signature fusion, the ability to learn, detect and classify new threats without being explicitly programmed, and, ultimately, increase accuracy of detection,” Thoi Nguyen, program manager for the Next Generation Explosive Trace Detection program at DHS’ science and technology directorate, said in a statement published Friday.

Alakai will continue to develop its Agnostic Machine Learning Platform for Spectroscopy designed to detect hazardous chemicals from spectroscopic instruments as part of the two-year SBIR Phase II contract.

PSI will use the SBIR funding to continue to work on its deep learning algorithm meant to detect and classify opioids, narcotics and trace explosives for optical spectroscopic platforms.

DHS said it expects the awardees to come up with a prototype for demonstration and evaluation for Phase III funding. Under the third phase, the companies will seek private funding to bring their technologies to market.

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Today, AI and ML are transforming the face of education technology.

Today, AI and ML (Artificial Intelligence, Machine Learning) are creating havoc in a number of fields and industries, including education. As per report of Ideamotive, according to Market Research Engine, the global AI in the education market will reach $5.80 billion by 2025 at a compound annual growth rate of 45%.

The following are some of the ways that machine learning and artificial intelligence are transforming the face of education technology:

An Approach to Learning That Is Both Structured and Personalized

The use of machine learning and artificial intelligence has assisted in the development of a more effective and time-saving approach for teachers to follow a clear ride of guidance. This has aided students’ comprehension and has gone beyond the criteria of human intelligence. Many ed-tech companies have started to deploy digital programs to boost the educational experiences, as AI in ed-tech helps respond to students’ specific needs. It helps students in assessing their success on various subjects and keeps track of their input.

Augmented and Virtual Reality

It is one of the most exciting advances in the area of AI and machine learning. Many colleges and universities are using this advanced technology to clarify life-like experiences in disciplines such as biology, astronomy, geology, and others. Students are able to interact with different topics using AR/VR technologies that include animations, pictures, movies, and more. This technology has proven to be the most effective means of assisting teachers and administrators in obtaining extremely accurate subject-oriented experiences.

Choosing the Best Profession

AI and machine learning will assist students in overcoming their dilemmas and predicaments when it comes to choosing the right career direction. Many times, poor decisions on this crucial front have resulted in the futures of millions of talented students being jeopardized. Fortunately, in the future, AI and machine learning will be able to save students from the regrettable pain of self-inflicted career destruction. All of these tools have excellent data mining techniques, which inevitably provide deep insight into students’ interests and despises, as well as their long-term objectives.

For Students with Special Needs, AI and Machine Learning is a Boon

AI and machine learning technology have proved to be an outstanding source of education for students with special needs. Many specially-abled students are encouraged to learn the subject through speech recognition and virtual reality technology, which enable them to effectively and ideally master even the most difficult topics.

What role does AI play in education?

AI is now being used in education, especially in the form of skill development tools and testing methods. When AI educational solutions develop, it is anticipated that AI will be able to help identify voids in teaching and learning, allowing teachers and administrators to do better than ever before.

There are several AI applications in education. Both teachers and students profit from the innovation. The education industry will benefit from AI in a number of ways. Byju’s, Vedantu like e-learning sites, in particular, are offering educational institutions a competitive advantage. It has introduced artificial intelligence (AI) e-learning software to connect with students and have more customized tutorials.

Conclusion

Education will be rapidly accelerated in the future, and learning needs will be even more diverse. Artificial intelligence can be extremely useful in identifying patterns before they take root and rapidly adapting to them.

The curriculum of the future educational institutions will be able to adjust as required. Additional teaching technologies will help students without putting undue pressure on teachers, and educators will be able to use their time and efforts more dynamically.

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To advance the use of machine learning in medical imaging, researchers will have to examine radiologists’ perceptions of the technology, as well as the cost-effectiveness of these tools, according to a study published in JMIR Medical Informatics. 

Countless studies have shown the diagnostic accuracy of machine learning tools. Organizations across the care continuum, from research universities to companies like Google, have developed machine learning algorithms that can identify breast cancer in medical images as effectively as human clinicians. 

While the results of these studies have promising implications for the future of radiology and pathology, JMIR researchers noted that more investigations may be necessary before machine learning tools can become part of routine clinical practice.

“Advancements in computer algorithms are becoming increasingly sophisticated and widespread in the field of radiology, with the potential to be cost-effective for increasing detection rates of various medical conditions and improve the efficiency of radiologists,” the team said.

“As we continue to head into an artificial intelligence era, it is essential that we understand the implementation of technologies in healthcare settings and its impact on health care providers and their potentially shifting roles.” 

The group analyzed nine peer-reviewed articles that focused on the implementation and adoption of computer-aided detection (CAD) in breast cancer screening. CAD, a form of machine learning, can help clinicians interpret medical images by acting as a double check or a second pair of eyes, replacing the typical double reading by a second pathologist. 

CAD scans digital mammograms and marks areas of potential cancer, which pathologists then review to reach a final assessment of the image. Although the use of CAD has increased significantly over the past several years, researchers stated that studies have largely overlooked radiologists’ perceptions of the technology, as well as its cost-effectiveness and efficiency. 

After reviewing past articles, the team found that incentives for adopting CAD included improved cancer detection rates, breast imaging profitability, and less radiologist time taken. 

However, researchers also found that providers didn’t have an overly positive view of the technology. In general, radiologists had more favorable perceptions of double reading by a colleague rather than single reading with CAD. One study showed that 74 percent of radiologists believed double reading improved cancer detection rates, while just 55 percent thought that CAD improved detection rates.

Additionally, the group found that the use of CAD was associated with higher interpretation times. CAD may take less time than double reading by a second radiologist, but researchers saw that when radiologists reviewed CAD-marked images, the mean interpretation time increased by 19 percent. 

CAD implementation was also associated with a significant increase in recall rates, which occurs when a patient is called back for follow-up imaging. Moreover, the use of CAD for breast cancer screening can be associated with higher financial costs, depending on the accuracy of CAD, the number of patients screened, and comparison with single versus double reading. 

These results indicate that more research is needed to identify and overcome barriers to machine learning adoption in the medical imaging field. 

“Through our scoping review of the adoption and implementation of CAD in clinical settings for breast cancer detection and other related articles, CAD use by radiologists is based on trade-offs between the barriers and facilitators,” researchers said. 

“The use of CAD for breast cancer screening involves several tradeoffs including weighing the impact on detection rates and patient outcomes, costs and financial incentives, time saved from double reading, increased recall rates, and radiologist perceptions.”

The study was limited in that researchers reviewed only a small number of articles. However, the results indicate that further research is needed to assess the implementation and adoption of machine learning in medical imaging.

“Our review suggests that there is a large focus on the diagnostic accuracy of CAD, but little focus on CAD implementation and perceptions of radiologists—the end users,” researchers said.

“We propose that further studies be carried out to better understand CAD adoption and implementation in clinical settings. Specifically, there should be a focus on investigating radiologists’ perceptions of CAD use in various settings, as we only came across one such study based in the United States, which cannot be generalized to other settings and health care systems.”

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