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While still a relatively new tool in healthcare, artificial intelligence (AI) and machine learning (ML) technologies pose immense opportunities to drive industry-wide transformational advancement. Yet, we have a long way to go as medical devices become more complex and paradigms for developing AI algorithms are not transferable from the consumer to the clinical space.

Given the promise AI and ML offer in improving patient care and the challenges facing medical device engineers face as they adapt and refine these capabilities, the conference at Medical Design and Manufacturing (MD&M) West is heavily focused on ideating powerful solutions that will advance the maturation of healthcares advanced technologies. Slated to take place April 12-14 at the Anaheim Convention Center, MD&M West runs in tandem with four other engineering trade shows WestPack, ATX West, D&M West, and Plastec West, providing attendees access to a five-in-one design and manufacturing event and the Design. Engineer. Build. conference that connects multiple verticals and industries.

With the event on the near horizon, I spoke with renowned medtech expert and prominent MD&M West speaker Siddharth Desai, president of HealthCare Evolution. We discussed industry trends and what attendees can look forward to learning in his technical and forward-looking session.

Desai: AI and ML are invading the classical new product development domain in various ways and will change the future of medical devices. AI is an interdisciplinary approach to provide intelligence in diagnosis, treatment, and post-treatment assessment of the clinical challenges. This discipline is based on the development of firmware and algorithms; the medical devices are learning and able to provide an efficient diagnosis of the desired treatments and recommend and enable the clinician/healthcare provider solutions.

These treatments are viable because of the analytical capabilities, computing power, and firmware algorithms developed by the most brilliant engineers and scientists. So, these capabilities are incorporated in the medical devices as incremental features to the base devices and are added as modules or incorporated as separate devices. The applications for the AI systems are diverse and across the entire field of healthcare: "smart implants"that assess and enhance a total knee arthroplasty and the recovery from the treatment;"smart algorithms" that improve the ability to diagnose tumors, enhance the image of malignant tumors, and enable more effective treatments in endoscopy;"smart AI powered"infusion treatments that provide efficient dosage for a specific treatment to the clinician and the pharmacists;"smart"data analytics that renders the suggested optimumtreatment to a clinician:and the patient who can use the AI reports to improve personal healthcare. AI/ML/DL is a vast opportunity and can be applied in the vast domain of healthcare.

Desai: Since the discipline is emerging, the challenges are unique to its applicability. First and foremost is the idea that a "smart machine"can replace a clinician. Working with key opinion leaders and everyday clinicians, we realize there is a certain level of skepticism. Clinicians go through multi-year training and hands-on work to get comfortable with the clinical practices. So, the adaption curve and overall success will be based on solid AI/ML/DL answers.

Secondly, the discipline must prove itself! While bold predictions can be made, the clinical space and human beings are unique, and the ability to predict the outcome at large will be a unique challenge. If the model developed by the engineers is inadequate or incorrect, the AI solutions may have serious consequences. So, in the seminar, we will discuss the critical success factors, planning for the solutions, and relevant case studies. We also define the basics of a business case to assess the opportunities.

Desai: Ours is a directly relevant session for those involved in the planning, development, and management of medical device innovation. We have a fantastic lineup of speakers who will discuss the best practices and share their experiences in medical device design and development.

The speakers will discuss and share their product design and development expertise from the entire product lifecycle, from concept to product launches. We will discuss critical aspects of development processes: capturing user inputs, detailed design and development processes and lessons learned, material selection processes, learning from world-renowned experts in TAVR development, and the essence of artificial intelligence. We will make this session an interactive, relevant, and exciting place. Our mission is to learn and share our experiences such that the attendees have a good learning experience as well as have some fun.

Desai: I have been involved in the medical device industry since 1983. Having worked for startups to large corporations, I have enjoyed being a catalyst for change. The industry has been equally kind to me; I have enjoyed success as a contributor to a leader. I feel obligated to learn and give back! Improving human lives excites me and enables me to think in the box and outside the box. It continues to challenge me to learn new things.

I am excited to share our session, my personal experiences, and meet and learn from fellow conference participants. The pandemic has had an impact on all of our lives. But, as life returns to normal due to the innovation of vaccines, the medical diagnosis industry with COVID-19 testing and facemask development is the direct contribution by our industry. We intend to share critical lessons learned, delve into innovation that is underway, and discuss the future as we can envision in 2022 and beyond.

To tune into Siddharths presentation Artificial Intelligence Platforms and Next-Gen Applications & Use Cases, register to attend MD&M West here.

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Your Medical Device is Getting Smarter and More Reliable! - Medical Device and Diagnostics Industry

LAS VEGAS--(BUSINESS WIRE)--Tachyum was highlighted as one of the Most Innovative Artificial Intelligence Solutions Providers to Watch by The Enterprise World and earned the featured cover story with an in-depth discussion about the company, its leadership, vision of the industry and the AI attributes of its Prodigy universal processor.

The Enterprise World, with its wide topics for every month, brings to readers new and changing trends in business, market growth, changing government reforms and the growing customer base of a particular industry. As part of each issue, the magazine features the success stories of people from the enterprise world with exclusive interviews that will help readers learn different and efficient ways to run their businesses. Tachyum is featured in the latest issue with an in-depth profile of the company, its founder and CEO Dr. Radoslav Danilak and how Prodigy is going to impact the future of AI.

As AI migrates to more sophisticated and control-intensive disciplines, such as Spiking Neural Nets, Explainable AI, Symbolic AI and Bio AI, Prodigy will deliver an order of magnitude better performance than its competitors, said Danilak. Prodigy-powered universal servers in hyperscale data centers, during off-peak hours, will deliver 10x more AI Neural Network training/inference resources than currently available and since the Prodigy-powered universal computing servers are already bought & paid for, the expense of operating such systems will be extremely low. I look forward to sharing details of our success in AI with readers of The Enterprise World.

Tachyums Prodigy processor can run HPC applications, convolutional AI, explainable AI, general AI, bio AI, and spiking neural networks, plus normal data center workloads, on a single homogeneous processor platform, using existing standard programming models. Without Prodigy, hyperscale data centers must use a combination of disparate CPU, GPU and TPU hardware, for these different workloads, creating inefficiency, expense, and the complexity of separate supply and maintenance infrastructures. Using specific hardware dedicated to each type of workload (e.g. data center, AI, HPC), results in underutilization of hardware resources, and more challenging programming, support, and maintenance. Prodigys ability to seamlessly switch among these various workloads dramatically changes the competitive landscape and the economics of data centers.

The article is available in the February 2022 issue of The Enterprise World.

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About Tachyum

Tachyum is transforming AI, HPC, public and private cloud data center markets with Prodigy, the worlds first Universal Processor that delivers industry-leading performance, cost, and power efficiency for both specialty and general-purpose computing. When Prodigy processors are provisioned in a hyperscale data center, they enable all AI, HPC, and general-purpose applications to run on one hardware infrastructure, saving companies billions of dollars per year. A fully functional Prodigy emulation system is currently available to select customers and partners for early testing and software development. With data centers currently consuming over 3% of the planets electricity, predicted to be 10% by 2025, the ultra-low power Prodigy Universal Processor is critical if we want to continue doubling worldwide data center capacity every four years. Tachyum, Co-founded by Dr. Radoslav Danilak with its flagship product Prodigy, is marching towards tape out targeting Q2 2022, with software emulations and an FPGA-based emulator running native Linux available to early adopters. The company is building the worlds fastest 64 AI exaflops supercomputer in 2022 in the EU with Prodigy chips. Tachyum has offices in the United States and Slovakia. For more information, visit https://www.tachyum.com/.

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Tachyum Named Among the Most Innovative Artificial Intelligence Solutions Providers to Watch by The Enterprise World - Business Wire

Today, there are more and more vehicles on our roads, which creates an increased risk for accidents and associated injuries and deaths. Globally, about 1.3 million people die each year as a result of traffic crashes.

With artificial-intelligence technology paving pathways for many industries, transportation is beginning to utilize the benefits of AI to increase driver safety, driving and overall road safety.According to market research, the global artificial intelligence market size was valued at $51.08 billion in 2020 and is projected to reach $641.30 billion by 2028. Manufacturers have identified multiple ways AI can be utilized on the roads and ultimately provide an increase in overall safety and comfort for drivers and passengers.

Primary Use Cases for AI in Transportation

From cabin safety to road conditions and urban planning, AIs ability to detect and record patterns can provide drivers with information needed to make accurate decisions.

Primary use cases include:

Traffic Management

AI in transportation utilizes closed-circuit TV cameras and sensors that can record valuable insights while on the roads to pick up on traffic conditions and prepare drivers for delays on the way. This data is stored through cloud AI or Edge AI systems that create a quicker way to store traffic pattern recognition to predict the status of roadways.

Edge AI technology is a method of faster computing and can enhance the overall performance of applications based on AI and increase the accuracy with its deep learning capabilities.

Fleet Management

Logistics companies have begun to use AI to keep up with the increasing delivery demands across the world. Companies such as Amazon require 999 out of 1,000 deliveries to be made on time, which puts lots of pressure on drivers. AI-based technology plays a large factor in keeping drivers safe in high-stress environments, as well as making route corrections for efficiency.

Researchers predict by 2023, the global transport AI market will reach $3.5 billion. The implementation of AI in logistics companies allows for the measurement of driver behavior and performance, assisting with human decision making, fleet visibility, predictive repair and maintenance, and predicting the most fuel-efficient routes.

Increasing Public Safety

AI not only can detect objects, but also can differentiate between inanimate objects and people. The technology even has been useful in detecting pedestrian traffic. In major cities, pedestrian crossings can create a large potential for accidents.

Dashcams help allow the driver to see all sides of the vehicle as well as sensors detecting any objects around the vehicle. Todays AI-based dashcams and sensors combine integrated technology to detect pedestrian walkways a few miles ahead to equip drivers with the best knowledge possible for navigating pedestrian-heavy cities.

The Future of AI

AI is transforming the automotive industry in more ways than one.

More innovations are being created all the time with vehicle-to-vehicle connectivity being at the forefront, which allows for the sharing of vehicle information such as speed, location and any hazards on the road. Overall, AI is prioritizing safety and helping drivers manage busy roadways and be aware of hazards.

Claude Hochreutiner (pictured, above left)is director-Platform & Data Managementfor Smarter AI.

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Artificial Intelligence Doing More to Increase Driver Safety - Ward's Auto

TORTOLA, BRITISH VIRGIN ISLANDS / ACCESSWIRE / March 4, 2022 / Galaxy Trading Analytics (GTA), headquartered in the British Virgin Islands (BVI), is a technology company specializing in world-class artificial intelligence. GTA is empowering cryptocurrency investors and traders with a novel approach to growing their portfolios amidst the Covid-19 pandemic.

Initially developed for private clients and institutions, they are now offering their GTAI (Galaxy Trading Artificial Intelligence) System to the masses, which have developed a reputation in the industry with consistent trading profits.

GTAI System, the World's First Hybrid AI Trading and Arbitrage Software Bot, is designed to help crypto traders maximize trading profits while minimizing risks and losses.

Arbitrage has been a strategy utilized by investors and traders in growing their portfolios by leveraging price asymmetries and inefficiencies across different exchanges or markets. As a method of trading, it requires a high level of expertise, experience, and involvement, thus making it inaccessible for most.

Technology is changing this, by providing platforms that make it accessible to traders by utilizing artificial intelligence, algorithmic trading, and lightning-fast transactions in successfully executing arbitrage for portfolio growth.

Unlike traditional arbitrage bots that only deploy triangular arbitrage, GTAI deploys 4 different trading strategies, making it more stable and profitable even in bull, bear or volatile market conditions. In the future, GTA will even implement more proven trading strategies into the GTAI System.

The GTAI system is monitored 24 hours a day, 7 days a week by a dedicated team, deploying the right strategies and risk management protocols according to the market conditions.

About Galaxy Trading Analytics

Galaxy Trading Analytics, GTA, is a British Virgin Islands based regulated fintech company established in 2022, with an office in Canada, and teams operating around the world. With a strong team of Artificial Intelligence and Deep Learning experts since 2013, their core focus is to develop niche investment solutions and investment advisory tools. GTA manage and maximize their clients' assets via their AI technologies, GTAI system and a user-friendly mobile App, giving them the best yield in the crypto markets with minimal risks.

Media DetailsMike Peterson media@gtatrade.comTortola, British Virgin Islands gtatrade.com

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SOURCE: GTA Trade

View source version on accesswire.com: https://www.accesswire.com/691560/Artificial-Intelligence-by-Galaxy-Trading-Analytics-Enables-Greater-Access-to-Portfolio-Growth-and-Diversification

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Artificial Intelligence by Galaxy Trading Analytics Enables Greater Access to Portfolio Growth and Diversification - Yahoo Finance

Artificial intelligence (AI) is taking the technology industry by storm. We see a surge in solutions embedded with virtual assistants and chatbots, with large enterprises integrating AI across the entire tech stack. A recent report suggests that the global AI market will have a valuation of $190.61 billion by 2025, and the forecasted annual growth rate will be around 33.2%.

Artificial intelligence and related technologies are making our existing solutions even more intelligent and are helping us unlock the power of data. The machine learning algorithm, computer vision, natural language processing, and deep learning are now easy to bake into any solution or platform.

Artificial Intelligence can disrupt critical business processes like collaboration, control, reporting, scheduling, and more. In this blog, we will discuss ways for organizations to implement AI efficiently and effectively.

Research and Understand

First and foremost, get acquainted with what enterprise AI can do for your business. In addition to consulting with pure-play AI companies who can advise you on how best to go about this, you can also refer wealth of online information available to familiarize yourself. Some universities like Standford have online papers and videos on AI techniques, principles, etc. Your tech team can check out Microsofts open-source Cognitive Toolkit, Googles open-source TensorFlow software library, AI Resources, the Association for the Advancement of Artificial Intelligence (AAAI)s Resources, MonkeyLearns Gentle Guide to Machine Learning, and other paid and free resources available. More research gives you a head start, and you will know what you are getting into as an organization, how to plan for it, and what to expect at the end of it,

Pin-point the use case

Once you know what AI can do, the next step is to identify what you want AI to do for your business. Think of how to add AI capabilities to your products or services. Build specific use cases in mind around how AI can solve some of your challenges and add value to your business. For instance, if you review your existing tech program and its challenges, you should have a strong case around how image recognition, ML, or others can fit into the product and how useful it will be.

Attribute financial value

Once you have those use cases ready, assess the potential business impact of those and project the financial value of the AI implementations identified. Tying business value to AI initiatives will ensure you are not lost in details and always put outcomes at the center. The second part is to prioritize AI initiatives. Put all your initiatives in a 2X2 matrix of business potential and complexity, and that will give you a clear picture of which ones to go after first.

Identify skill gaps

Once you have prioritized your AI initiatives, its time to check if there are enough ingredients in the kitchen. Its one thing to be wanting to accomplish something and the other to have an organizational capability for it. Before launching a full-blown AI implementation, you can assess your internal capacity, identify skill gaps and then decide on a course of action. You may hire additional resources, or you can tie up with pure-play product engineering companies specializing in AI.

Pilot under the guidance of SMEs

Once you are ready as a business, start building and integrating AI within the business stack. Have a project mindset, and importantly ensure that you dont lose sight of business goals. You can consult with Subject Matter Experts in the space or external AI consultants to ensure that you are on track. Your pilot will give you a taste of what long-term implementation of an AI solution will involve. The pilot will make the case even strong, and you can decide if it still makes sense for your business. But for the pilot to succeed, you will need a team of your people and people who know AI to keep it impartial. Having external SMEs or consulting partners is a great value add at this stage.

Massage your data

High-quality data is the basis of a successful AI/ML implementation. It is critical to clean, massage, and process your data to get better results. Usually, data for enterprises is in multiple silos and various systems. Form a small unit, especially cross-functional, to integrate different data sets, resolve inconsistencies, and ensure that the output is high-quality data.

Take baby steps

When you start, start small. Apply AI to a small data set to test thoroughly. Then incrementally, you can increase volume and collect feedback continuously.

Plan for Storage

Once your small data set is up and running, you need to start thinking about additional storage to implement the full-blown solution with complete data input. The algorithms performance is equally important as its accuracy. To manage large volumes of data for better accuracy, you need a high-performing solution supported by fast and optimized storage.

Manage the Change

AI provides better insights as well as automation. But its a big change for employees as it expects them to operate differently. Some employees are warier than others, and they must accept the change positively. You will need a formal change management initiative to introduce the new AI solution augmenting their daily tasks.

Build Securely and Optimally

Usually, companies start building AI solutions around specific aspects or challenges without studying the limitations or solution requirements as a whole. It will result in sub-optimal or dysfunctional solutions and sometimes insecure too. You will need a balance of storage, the graphics processing unit (GPU), and the network to achieve an optimum. Security is also mostly overlooked, and most companies realize that post-implementation. Make sure you have security safeguards in place like data encryption, VPNs, anti-malware, etc.

AI implementation is no cakewalk, and challenges may arise at every step. But with every technology, the challenges associated with the adoption are the most difficult to tackle. Data literacy and trust are the two pillars of introducing any new technology. Another important aspect of AI initiatives is that it matures with your data management strategy. You will need both of them to run in parallel for success.

Views expressed above are the author's own.

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10 steps to implement artificial intelligence effectively in your business - Times of India