Although software is supposedly “eating the world,” it is heavily restricted in doing so by access to development talent and the increasing number of tasks required to build software. The number of jobs requiring software developers is increasing at a rate that vastly outpaces the number of skilled professionals entering the market to fill those roles. Even for those already in a programmer role, most of their time is not necessarily spent coding new features but rather writing tests, patching security issues, reviewing code, and fixing bugs. These two factors make it even more important to boost the productivity of those in the workforce, and the most recent improvements in AI-driven natural language processing (NLP) models are making that a reality. By virtue of their size, underlying architecture, and training data and regime, the newest generation of state-of-the-art NLP models—called generative pre-trained transformers (GPTs)—can translate between many languages, including from text to code. Embedding this powerful ability into tools that developers can use is already proving invaluable at making developers better at their jobs and unlocking software production for less technical folks.

Software Struggles to Eat the World

Along with the explosion of software for all sorts of use cases and business applications in the past decade plus, an equivalent has emerged for the number of jobs that require coding experience to build that software. A survey by Code.org in 2017 showed an estimated 500,000 open programming roles available in the United States alone, but unfortunately, many go unfilled, especially since only 43,000 computer science graduates enter the market every year, a figure that has decreased in recent years. On top of that, the time usually taken to become skilled enough to take those jobs ranges from 3 to 5 years; by the time a programmer is ready to enter a more senior role, the number of available roles will have increased by 28% (U.S. Bureau of Labor Statistics). As such, the bottleneck of talent stymies many companies’ attempts to build impactful software.

Once a team of developers is in place within a company, the challenges don’t stop. Requirements for building software, particularly in terms of quality, security, and speed of delivery, are increasingly complex. Surprisingly, developers will spend only about 30%–40% of their time developing new features or improving existing code (Newstack). This is because a large portion of their work also includes writing tests, making fixes, and solving security issues. Senior developers will also spend a portion of their time mentoring the juniors on their team and performing code reviews. All of these elements combined can impact delivery speed and cost-effectiveness of a software project. It also provides the perfect opportunity to pair AI with human developers to help address a lot of these drawbacks.

AI + Developers = Pair Programming Dream Team

AI-driven Coder Tools

With the strides that deep learning–based NLP has made in the past couple of years, a number of tools have emerged with AI at their core, designed to improve the productivity and code quality of developers. In particular, the models that these tools use can parse code to identify bugs and flaws, effectively performing some of the more tedious parts of a code review. A few such tools released recently, like CodeGuru and DeepCode, were able to find vulnerabilities that were difficult for humans to identify as well as find that 50% of the pull requests studied had issues (AI-News).

Additionally, modern NLP techniques improve developers' code quality and speed up development by helping auto-complete sections of code, monitor their code output for errors, and even auto-generate unit tests. OpenAI’s Codex algorithm, which was incorporated into GitHub’s Copilot, can do this with astounding accuracy, even going so far as to generate code from human language input (Figure 1). Its ability to do this comes from the data it was trained with, both natural language fragments and a vast amount of code. A preliminary study from GitHub on its performance showed that for a mundane task like writing an HTTP server, leveraging AI alongside a developer reduced time to completion by half. The fact that the model underpinning this tool can auto-complete entire code sections from a single comment also makes coding vastly more accessible to beginners and less technical folks.

Coding Made More Accessible

The powerful AI translation capabilities of this new generation of NLP models means that, within some limits, anyone can use human language to produce snippets of code that they need. These pieces of software can be in any programming language as well, from those used to execute certain routines like Python, JavaScript, and C++ to those used to access data in various databases like SQL and NoSQL. Provided that the program a person would like to code is not overly complicated, tools like Codex can likely help. It has proven useful for making small websites, deriving excel functions, and converting what a user would like from human language to the query language used to access the data. However, as the researchers of these techniques note, the models are not totally correct all the time. Often, the code produced is mostly correct but requires some intervention from an experienced developer. In this sense, the models can boost the productivity of a human coding tutor in that they can take over where the AI runs into issues as it is being used by a less experienced person. This can also mean that junior developers’ productivity is greatly increased while their requirement for supervision and senior input is reduced.

From Human Language to Computer Language

So how do most of these tools work? The main workhorse powering these innovative tools is the GPT—typically the third generation of such models, called GPT-3. This architecture was initially developed by OpenAI and trained on a massive amount of text from across the internet, including prose, digital books, tweets, open-source repositories, comments, online articles, and more. Although the goal was always more realistic language generation, the side effect of the model also being able to generate code led to the later development of Codex.

Several factors separate GPT generations from previous deep learning–based NLP models. These include the amount of data used for training and the way in which the models were trained as well as the number of parameters the models have and their state-of-the-art underlying architecture. Since these models and their predecessors are neural networks, the number of parameters influences the complexity of relationships in data that they can capture, meaning that bigger models can learn more nuanced patterns than smaller models. These models were also trained in a multitask setting and in a self-supervised fashion. Most neural networks are made to perform a single task and as such take specifically-labeled data in order to learn how to do that task—a great example being AlphaGo, which is great at GO but cannot play chess. Requiring labeled data is called supervised learning. The GPT-3, by contrast, was trained to predict the next word in a sequence, so the data don’t need labeling; this is the backbone of many tasks like translation, text generation, and question answering.

Additionally, there are transformer models, which perform better than previous NLP benchmarks like long short-term memory models or recurrent neural networks. They process entire sentences at once instead of word by word, store and use similarity scores between words in a mechanism called “attention,” and encode information related to a specific position of a token in a sentence (Figure 2). All of this allows for a bigger model that can learn more since parallelization and recursion are no longer issues, as they were in the past. It also removes the difficulties previous models had with forgetting relationships between far-apart words and sentences. Since the GPT-3 is available through an application programming interface provided by OpenAI, it can be incorporated into other AI for coding products, which democratizes access to coding even further.

Conclusion

In order to continue building the vast amount of software needed to cover all the applications that require it, AI is stepping in to help boost the productivity of developers. Since the demand for programmers clearly outstrips the supply, leveraging other solutions to augment the output and code quality of coders companies already have is proving more and more beneficial. With the recent dramatic improvements in AI-based NLP models, such as the particularly powerful GPT-3, the dream of an AI-powered pair programmer for human developers is becoming a reality. With such models embedded in their everyday tools, programmers can stand to gain a lot in terms of reduced time spent on repetitive tasks like writing tests and improved code quality from automated reviews and auto-generated snippets. Even junior developers and less technical folks can benefit from the text-to-code capabilities now available. Software may not be able to eat the world alone, but AI can certainly help.

About the Author

 Becks is a fullstack AI lead at Rogo, a New York-based startup building a platform to allow anyone to analyze and gain insights from their own data without a background in data science. In her spare time, she also works with Whale Seeker, another startup using AI to detect whales so that industry and these gentle giants can coexist profitably. She has worked across the spectrum in deep learning and machine learning from investigating novel deep learning methods and applying research directly for solving real world problems to architecting pipelines and platforms to train and deploy AI models in the wild and advising startups on their AI and data strategies.

Original Source: Mouser

As consumers consider lifestyle changes following the COVID-19 outbreak, they have prioritized monitoring and augmenting their health to pursue a healthier lifestyle. As a result, the wearable healthcare devices market is growing significantly, from an expected USD 27.29 billion by 2022 to an anticipated USD 324.65 billion by 20321. Improved reliability and speed of 5G and higher networks, coupled with the development of new technology, are driving this growth. In addition, unique healthcare needs and design forms have enabled emerging markets to meet consumer demand for new opportunities in this space. This post reviews three major healthcare wearable device types to help consumers improve monitoring and overall health and safety.

Rings for Preventative Care and Sleep Monitoring

Smartwatches have been the standard for wearable health monitoring; Fitbit has become synonymous with health monitoring watches, with products from Apple, Samsung, and Google providing excellent, readily available alternatives. But a fundamental problem with the wrist as a biomedical monitoring point is the relative weakness of the signal strength. Early watches collected massive amounts of data and regressed trends, but with suspect accuracy; this approach rendered the data useless to medical professionals.

Compared to the wrist, the biomedical signals in the fingers are up to ten times stronger2. In an effort to better detect and measure the user's vital health metrics, designers developed wearable smart rings that use the fingers as the biomedical monitoring point. The ring includes highly-sensitive temperature sensors, accelerometers, infrared LEDs, and gyroscopes to provide measurement accuracy at the level of professional medical equipment.

Rings are much more comfortable for the user to wear during sleep and provide real-time measurements for heart rate (and changes), sleep cycles, body temperature, physical activity levels, and respiration. The accuracy of the electronic components is critical, delivering more accurate data with significantly reduced computational load due to precise local measurements.

Emergency Bracelets and Pendants

While many healthcare wearables intend to integrate into daily life transparently, healthcare pendants are an emerging device helpful for seniors in sudden, life-threatening situations. They can provide 24-hour monitoring, GPS location tracking, and detect a fall event. In addition, some devices have bi-directional communication capability with family or emergency personnel via 5G cellular networks or Wi-Fi®.

While the bracelets and pendants accompany the user without using their hands, much of the operating technology is the same. For example, embedded accelerometers aid in fall detection, an essential feature for seniors given that 90 percent of seniors not receiving medical help within six hours become dependent on a nursing home3.

Transdermal Patches for Medicine Administration

Another disruptive form factor for healthcare wearable technology is a transdermal patch that administers medicine through the skin. These patches contain a predetermined drug dosage, consistently delivering it to the bloodstream through the skin. While the concept is simple, both active and passive administration systems exist for transdermal patches.

Passive systems rely only on natural diffusion through the skin. Therefore, the administration rate can vary depending on the user’s skin characteristics and the unique patch designs. Conversely, active dosage in the body is more complex. This approach can employ microneedles, chemical enhancers, or a mild electrical current, on the order of 2-10 mA4, to drive the medicine into the skin at a designated time. These features allow improved customization for patient treatment.

The electric current transmits the medicine into the skin through iontophoresis, which requires direct current to administer treatment from the patch to the skin in one direction without interruption. A machine applies the current, accelerating the medication delivery to the skin. This approach is advantageous for ADHD, and anti-inflammatory treatments as users have varying needs for the amount of medicine they receive.

Conclusion

Like any consumer product, even those with clear and necessary benefits like healthcare monitoring, aesthetics, and cost will be critical factors in their adoption. Consumers will likely value the advantages, but not if the device looks and feels like a bulky meter—they will actively compare the cost and look of a device with its benefits. As a result, device engineers must consider integrating electronics’ functionality within the envelope of marketing insights to design products that consumers will actually wear. Clearing that hurdle will unlock mass adoption of new healthcare wearable devices, improving consumer health by increasing access to preventative care.

Wearable health devices have progressed significantly from early smartwatches. They are more accurate, address more aspects of health, and have become vital tools for user preventive, emergency, and treatment care. As technology continues to improve healthcare, physicians and users alike can rely on wearable devices to enhance their quality of life in a non-invasive, proactive way.

About the Author

Adam Kimmel has nearly 20 years as a practicing engineer, R&D manager, and engineering content writer. He creates white papers, website copy, case studies, and blog posts in vertical markets including automotive, industrial/manufacturing, technology, and electronics. Adam has degrees in chemical and mechanical engineering and is the founder and principal at ASK Consulting Solutions, LLC, an engineering and technology content writing firm.

Original Source: Mouser

Of late, a report of a well-known media stated that India's Electronic Security Market was valued at USD 1.32 billion in 2021 and is expected to reach USD 4.90 billion by 2027, at a CAGR of 24.37 percent over the forecast period. The homeland and defense security sector has tremendous potential and since then the growth of security products has changed and now, the country has several manufacturers from the security electronics domain. The security products industry is growing rapidly and is estimated to escalate by 22 percent per annum, which will sustain by 2025 and beyond.

India is emerging as a global power and security is emerging as a priority. We need to ensure India must grow by itself and must be a source of prosperity for the rest of the world. The size of the global security electronics market is intense and valued at US $41 billion in 2020 and is expected to reach US $68 billion by 2026 at a CAGR of 9 percent from 2021-2026. Experts claim that the country must target a 20-25 percent share of this global market in the next five years because it has a potential of US $15bn. India’s CCTV market is expected to grow at a CAGR of 22 percent from 2021-2026. Overall, the video surveillance market in India is estimated between Rs 11,000 crores of which 50 percent are CCTV cameras. India now needs to stay alert and on top of emerging technologies to counter successfully.

Strategically, the defense sector of the country is very imperative and the overall budget approved by the government for the Indian armed forces for the FY 2019 was 60.9 billion whereas, for the FY 2020-21, the expense was sanctioned US$65.86 Billion, which is just an escalation of 7 percent. The nation is now opting for higher indigenization defense hardware as the country still imports 70 percent of its top-notch hardware like missiles, aircraft, submarines, and ships mostly from the US, Russia, Japan, and Israel. In the defense security market, India is currently in a much better position because there is a huge market that is opening up for the country in defense equipment manufacturing. Earlier, India was mostly an importer, but now the government is promoting manufacturing in the country and now, a lot of global players like Tata, L&T, Hanwha Group, etc are investing in India. Anil Mehra, Senior VP, Matrix Cosmec opined that if we leave out the top eight countries or the major European nations, others are also looking forward to perk-up their defense manufacturing ecosystem and they all import their defense equipment. So, India could be a good source for most countries for any kind of defense equipment and ammunition.

Mehra said, “Coming to homeland security, there are a lot of opportunities that exist in India. This market is approximately Rs 15,000 crores and they are mostly dominated by MNCs. Though there are some companies who claim to be making Make in India products, they are screwdriver companies because they are just assembling and claiming to be Make in India. This is where compliance needs to be enhanced when the government makes those purchases. They have to really verify what is true Make in India and who has got the infrastructure. Video surveillance is about an Rs 8-10000 crore market. Even if I exclude the IT components; the market for cameras and software is still about Rs 6000 crores. The opportunity can be realized only if we have backward integration like a strong component manufacturing in the country.”

Mehra further highlighted the key challenges of the sector which is how India can become self-reliant for all security products. Another major hindrance is the availability of components at the right cost. Then, there is a compliance load on the SMEs like GST are very complex, which needs to be streamlined. The industry must interface with the government and understand the needs of each other. Another big challenge is the education system or the talent pool of the country because nowadays people opting for electronics and telecommunication are missing. Some of the colleges have stopped offering this course and moved towards software. India wants to focus on manufacturing and become self-reliant on that and if the talent is not available in the country then it's a very big challenge the nation is going to see in the next few years.

Amid the challenges and a couple of intricacies, experts also showcased a bigger positive picture of the sector. Currently, the government is carefully looking to address all the impediments related to defense and homeland security. According to Sanjeev Sehgal, managing director, Samriddhi Automations, the major concern of the government officials who visited during the IISE expo was how they can make the security infrastructures of the citizens more secure backed by Artificial Intelligence and other top-notch software features. He said, “The major requirement is now providing customized solutions for every customer because they are not getting the desired ROI out of it and hence they are getting dissatisfied. First, we have to understand the challenges of the customer and then come up with exact solutions for what they are looking for.”

“In the coming 5-10 years, the homeland security market will grow at a CAGR of 15-20 percent. Since India is known for offering top-notch software features, they are going to play a major role in the hardware equipment. Hence, I can assure that solutions appearing from Indian companies will be in much more demand globally, which will help us in giving tough competition to our neighboring countries and also to the US and Europe. There will be a complete integration of both hardware and software in the coming year,” added Sehgal.

According to a report by media shared from the Ministry of Defense, the country has now recorded the highest ever export of defense products worth Rs Rs 12,815 crore in the FY 2021-22 and Rs 1,387 Core for the quarter April-June 2022. It is an escalation of 54.1 percent compared to the preceding year. According to Minister of State for Defense Ajay Bhatt, the nation's defense exports were worth Rs 8,434 crore in 2020-21, Rs 9,115 crore in 2019-20, and Rs 2,059 crore in 2015-16. The concerned ministry has now undertaken numerous policy initiatives in the past couple of years to whip up state-of-the-art manufacturing, development, and design of defense equipment, which will ultimately promote self-reliance in defense technology and manufacturing in the nation. India has a quest for a defense export target of USD 5 billion in the coming few years. There is a wide range of initiatives such as the prime concern for the acquisition of capital items from domestic sources under Defense Acquisition Procedure (DAP)-2020, the proclamation of 18 key defense podiums for industry-led development and design, and allowing of FDI of 74 percent investment under the automatic route. The imperative strategic defense products comprising Unmanned Aerial Vehicles, Light tanks, and helicopters are included in the indigenous list for which there would be an official ban on the import beyond the schedules indicated against them.

The security industry encompasses a whole lot of issues. India is now well-equipped for the envisaged threats it has. As far as the threat perceptions are concerned India has adequate defense equipment and is in a very comfortable situation, but more innovation and quality assurance are needed, feel experts. Now, what needs to be done is we need to focus on strengthening our defense and security forces using the latest technology which is available because this is not just related to the volume of the defense products, but the quality that matters.

Justifying the requirement of quality checks and assurance, Air Vice Marshal Arvind Verma, Adviser-Aviation, Zen Technologies Ltd said, “The numbers have to be backed by the capability of each system you have. For example, 15-20 years back the number of soldiers China had or aircraft they had was huge but their combat potential was not good. We have always focussed on getting quality products in general. Therefore, when I say adequate capabilities, it does not mean volume, but they must have the potential to meet the challenges. We also require various equipment and technologies to be replaced urgently.”

Verma further added, “There is a requirement for Hindustan Aeronautics Limited (HAL) to improve its production capabilities, which are now quite low. They need to enhance and bring our inventory to a level that end-users feel comfortable with. Homeland security is also in the same line. There is also a need for huge intelligence networks, which need to be cohesive so that every piece of information is available. It will then help to take the required action on time. The major challenge is providing the right equipment to the forces and the quality and quantity must be matched.”