SwissCognitive Guest Bloggers: Bidyut Sarkar, Senior Solution Manager, IBM USA and Rudrendu Kumar Paul – Boston University, Boston, USA – “AI-Powered Predictive Maintenance in Advanced Manufacturing”

Takeaways:

• Deep learning offers unparalleled precision in predictive maintenance by analyzing intricate patterns in sensor data.
• Collaboration between domain experts and data scientists is crucial for effective model implementation.
• Embracing deep learning in maintenance strategies can lead to significant operational efficiency and cost savings.

Once a novel concept, predictive maintenance has evolved significantly with technological advancements. Historically, industries relied on rudimentary methods to predict equipment failures. However, the landscape transformed with artificial intelligence and deep learning. These cutting-edge technologies have ushered in a new era, offering unparalleled insights into the health and longevity of machinery. Deep understanding, in particular, has demonstrated its prowess by analyzing intricate patterns from sensor data, thereby enhancing the precision of predictions. This shift not only underscores the potential of modern algorithms but also highlights the transformative impact of technology on industrial operations.

In Europe, the market for machine learning (which includes deep learning applications) is forecasted to expand from $43.40 billion in 2023 to $144.60 billion by 2030, with a compound annual growth rate (CAGR) of 18.76% during this period.

Source: Statista

At the same time, the global predictive maintenance market is projected to grow to $64.3 billion by 2030, with a compound annual growth rate (CAGR) of 31% from 2022 to 2030. (Statista)

Source: Statista

The Limitations of Traditional Predictive Maintenance

Historically, predictive maintenance relied on essential monitoring tools and heuristic techniques. These methods, while foundational, often fell short of accurately forecasting equipment malfunctions. Relying on rudimentary sensors and manual inspections, traditional approaches needed more granularity to detect subtle anomalies or predict failures with high confidence. Furthermore, these techniques were susceptible to human error and could not adapt to the evolving complexities of modern machinery. Such limitations underscored the pressing requirement for innovations that could offer more detailed insights and higher predictive accuracy. As industries grew and machinery became more intricate, the inadequacies of conventional predictive maintenance became increasingly evident, paving the way for the integration of advanced technological solutions.

Deep Learning: A Game Changer for Predictive Maintenance

Deep learning, a subset of artificial intelligence, harnesses neural networks with multiple layers to analyze vast amounts of data; unlike traditional algorithms that plateau after a certain data threshold, deep learning thrives on extensive datasets, extracting intricate patterns often invisible to other methods. In the context of predictive maintenance, this capability is invaluable. (Swiss Cognitive)

Machinery, especially in industrial settings, generates a plethora of sensor data. This data, rich in minute details, holds the key to understanding the health and potential vulnerabilities of equipment. With their advanced neural structures, deep learning models efficiently sift through this data, identifying patterns and anomalies that might indicate impending failures. By doing so, these models offer a nuanced understanding of equipment health, allowing industries to address issues before they escalate preemptively.

The true prowess of AI (which includes deep learning) lie in its ability to discern patterns from seemingly random data points. In predictive maintenance, this means recognizing the early signs of wear and tear or the subtle hints that a machine part might be on the brink of malfunction. Thus, deep learning is a beacon of innovation, revolutionizing how industries approach equipment maintenance. (IJAIM)

Source: KSB

Real-world Applications and Case Studies

In the evolving landscape of predictive maintenance, several trailblazing entities have emerged, leveraging deep learning to redefine industry standards. Their applications provide compelling evidence of the transformative potential of this technology.

Uptake

One notable entity in this domain is Uptake, which has made significant strides in forecasting outages. By harnessing the power of deep learning, Uptake’s models analyze vast datasets to predict potential disruptions. The implications of such precise forecasting are profound. By averting unplanned downtimes, industries can optimize operations, reduce costs, and enhance overall productivity. Moreover, the ripple effect of these advancements extends beyond mere operational efficiency, influencing supply chains, labor management, and even environmental sustainability.

Augury

Another pioneer, Augury, has carved a niche in detecting nuanced data indicators that hint at equipment health. Traditional methods often overlook these subtle signs, but with deep learning’s intricate pattern recognition, Augury’s models can pinpoint anomalies with remarkable precision. Such capabilities enable industries to undertake timely interventions, ensuring machinery longevity and reducing the risk of catastrophic failures.

C3 AI

C3 AI stands out with its commendable achievement of over 85% accuracy in predictive analytics. Such a high degree of precision is a testament to the prowess of deep learning models that can sift through complex data structures, identifying patterns that would otherwise remain obscured. This accuracy bolsters confidence in predictive maintenance strategies and underscores the potential for further refinements and innovations in the field.

Delving deeper into specific applications:

• ML Forecasting Bearing Faults: Bearings, critical components in many machines, can exhibit faults that, if undetected, can lead to significant operational challenges. Deep learning models have demonstrated their capability to forecast these faults by analyzing vibrational data, temperature fluctuations, and other sensor outputs, ensuring timely interventions.
• Pump Cavitation Detection: Cavitation in pumps, where vapor bubbles form in the liquid due to pressure changes, can harm equipment health. Through deep learning, subtle signs of cavitation, often missed by conventional methods, can be detected, allowing for preventive measures.
• Predicting Wind Turbine Failures: Wind turbines, monumental feats of engineering, are not immune to wear and tear. When processed through deep learning algorithms, their vast data outputs can predict potential failures, from blade issues to gearbox malfunctions, ensuring optimal energy production and equipment longevity.

These real-world applications underscore the transformative impact of deep learning on predictive maintenance, heralding a new era of efficiency and precision.

Solution Strategies in Implementing Deep Learning for Predictive Maintenance

Incorporating deep learning into predictive maintenance is a nuanced endeavor, necessitating adherence to certain best practices to ensure optimal outcomes.

Model Governance

At the heart of any deep learning initiative lies the model itself. Ensuring its reliability and consistency is paramount. This involves rigorous testing, validation, and monitoring of the model in real-world scenarios. A robust governance framework makes the model behave as expected, even when encountering diverse and evolving datasets. Furthermore, documentation of model parameters, training methodologies, and validation results aids in maintaining transparency and trust.

Iterative Improvement

The dynamic nature of machinery and operational environments means that a one-size-fits-all model is a myth. As such, continuous refinement of deep learning models is essential. Industries can enhance predictive accuracy over time by revisiting and updating models based on new data and feedback. This iterative approach ensures that models remain relevant and practical, even in changing industrial landscapes.

Practitioner Collaboration

The success of any predictive maintenance initiative hinges on the synergy between data scientists and maintenance experts. While data scientists bring expertise in model development and data analysis, maintenance experts possess invaluable domain knowledge. Collaborative efforts between these professionals can lead to models that are not only technically sound but also contextually relevant. Such collaboration ensures that the insights derived from deep learning are actionable and aligned with on-ground realities.

Adhering to these best practices can significantly augment the efficacy of deep learning in predictive maintenance, ensuring sustainable and impactful results.

The Future of Predictive Maintenance with Deep Learning

The trajectory of predictive maintenance, guided by deep learning, paints a promising picture. As computational capabilities expand and datasets grow more affluent, the potential for refining and enhancing predictive models becomes increasingly evident. These advancements could lead to even more nuanced detections, capturing the minutest of anomalies that might have previously gone unnoticed.

Industries stand at the cusp of this transformative era, and preparation is crucial. Embracing a culture of continuous learning and fostering an environment conducive to innovation will be pivotal. Investing in training programs that bridge the knowledge gap between traditional maintenance practices and modern data-driven approaches can also prove beneficial. Moreover, as technology evolves, so should the strategies, ensuring that industries remain agile and adaptive.

In essence, the fusion of deep learning with predictive maintenance heralds a future marked by unparalleled precision, proactive interventions, and enhanced operational efficiency.

Challenges and Considerations

While integrating deep learning into predictive maintenance offers immense promise, it has challenges. A primary consideration is the data itself. Both quality and quantity are paramount; models trained on insufficient or skewed data can produce misleading results, potentially leading to costly misjudgments.

Additionally, the intricacies of machinery and equipment demand domain expertise. Mere algorithmic prowess needs to be improved. Collaborative efforts between domain experts and data scientists are essential to ensure that models are grounded in practical realities.

Lastly, concerns surrounding transparency and trustworthiness arise, as with any AI-driven initiative. Black-box models, which offer little insight into their decision-making processes, can be a source of apprehension for industries. Addressing these concerns through explainable AI methodologies and rigorous validation can help build confidence and ensure the responsible adoption of deep learning in predictive maintenance.

Conclusion

The fusion of deep learning with predictive maintenance signifies a pivotal shift in how industries approach equipment health and longevity. This synergy offers an unparalleled opportunity to detect intricate patterns, forecast potential failures, and ensure timely interventions. As the technological landscape continues to evolve, industries stand to gain immensely from these advancements, reaping benefits in terms of operational efficiency, cost savings, and machinery lifespan. Forward-thinking entities must recognize this potential and actively integrate deep learning methodologies into their maintenance strategies. Doing so, they pave the way for a future marked by precision, proactivity, and enhanced productivity.

About the Authors:

Bidyut Sarkar, Fellow of the IET (UK) and author of books on AI is an expert in life sciences and industrial manufacturing industry solutions with applied AI/ML experience, having served as a keynote speaker and judge at startup competitions. His professional experience has taken him to various parts of the world, including the USA, Netherlands, Saudi Arabia, Brazil, Australia, and Switzerland.

Rudrendu Kumar Paul is an applied AI and machine learning expert and the author of multiple books on AI, with over a decade of experience in leading data science teams at Fortune 50 companies across industrial high-tech, automation, and e-commerce industries. Rudrendu holds an MBA, an MS in Data Science from Boston University (USA), and a bachelor’s degree in electrical engineering.

Der Beitrag AI-Powered Predictive Maintenance in Advanced Manufacturing erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

Livia Spiesz, Head of Global Partnerships and Communications, SwissCognitive – “The AI Trajectory 2024 – Invest for Impact”

Venture Capitalism and AI: Seizing the Moment

In the past year, Artificial Intelligence (AI) redefined the scope of decision-making and operational efficiency and also revolutionized the capacity to solve complex challenges rapidly. This profound leap signifies the ushering of an era where AI underpins essential business strategies. Given the swift pace of AI innovation, venture capitalists (VCs) are increasingly drawn to its potential. Investment is not a matter of speculation but an imperative of the current landscape. The real question is no longer “Why invest in AI?” but “Can we afford not to?” Procrastination could mean forgoing the leadership in a wave of innovation that can transform society towards sustainable growth and resilience. Today’s investments are the seeds for tomorrow’s groundbreaking developments, casting a pivotal role in a revolutionized future.

AI Foundation Models: The Backbone of Tomorrow’s Technologies

At the center of AI’s rapid evolution are the foundational models, where leading LLM providers play a pivotal role. Consider NVIDIA, which started as a graphics processor manufacturer but has now become instrumental in AI infrastructure with its advanced GPU technologies. Current investments in this domain are reshaping how we perceive and interact with technology. When industry leaders articulate their visions, they do not merely set an organizational direction; they shape the global tech landscape. Their initiatives extend from optimizing search algorithms to driving comprehensive digital transformation projects, impacting lives across every longitude and latitude. These AI models aren’t just business – they’re the blueprints of worldwide digital advancement.

Revolutionizing Healthcare: Synergy of AI and Human Talent

AI in healthcare is not the future; it is revolutionizing patient care already today. AI-driven tools support doctors in swiftly diagnosing diseases, robotic arms perform intricate surgeries with meticulous precision, and intelligent systems analyze medical scans to catch signs of illness sooner than humans would do. AI is also personalizing patient treatment plans, managing hospital logistics to reduce wait times, and predicting patient outcomes for better resource allocation. Yet, alongside this AI-driven transformation is the irreplaceable value of human expertise. The real challenge, and opportunity, lies in harmonizing advanced tech with the nuances of human touch, intuition, and ethics. The synergy between cutting-edge technology and the depth of human skills presents a unique investment opportunity—one that promises returns in health, longevity, and quality of life.

STEM Evolution 2024: Redefining Science in the Age of AI and LLM

STEM (Science, Technology, Engineering, and Mathematics) fields are witnessing a renaissance as AI and LLM technologies redefine research paradigms. Leading academic institutions like MIT and Stanford are incorporating AI to amplify research, facilitating breakthroughs from synthetic biology to environmental sciences. The integration of LLM quickens data processing, refines hypothesis testing, and expands theoretical exploration, all at previously unimaginable velocities. This marks a pivotal moment for investors to contribute to a revolution that could unravel new scientific understandings and capabilities.

The Power of Collaboration: Uniting Minds, Industries, and Technologies

In a world characterized by its interconnectivity, collaboration emerges as the requirement of sustainable progress. Consider the collaboration between automotive giants and tech companies in creating autonomous vehicles, or pharmaceutical companies partnering with AI firms to expedite drug discovery. The magic happens when expertise from diverse fields converge, and silos break down. AI serves as the bridge to the future, but it’s human vision and collaboration that set the strong foundation and chart the path forward. In our pursuit of technological advancements, our greatest strength remains our ability to connect, share, and co-create.

Embarking on a Collaborative Voyage at The AI Trajectory 2024

The AI Trajectory 2024 is more than a conference; it’s a critical intersection where AI experts, industry leaders, and venture capitalists convene to shape the future. It’s a gathering grounded in real-world application and strategic foresight and offers a rare chance to engage with those at the forefront of AI innovation and investment. The discussions here are designed to cut through the noise, and focus on actionable insights that harmonize artificial and human intelligence to steer us into the future.

Join us at this conference and be part of a dialogue that shapes our collective tomorrow. As we map out the contours of a future enriched by AI, your participation is key to creating a vision that’s not only ambitious but also attainable and aligned with progressive growth.

Find the conference agenda and registration link HERE.
For two-click easy registration, click HERE.

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SwissCognitive Guest Blogger: Arek Skuza  – “From Startup to Industry Leader: Drizly’s Success Story Through Data Analytics and Innovation – Beyond Efficiency: AI’s Creative Potential”

Drizly is an online drink delivery service that has seen great success capitalizing on technology and making shopping for drink products easier than ever before. Founded in 2012, Drizly has since become a leader in online drink delivery, offering over 4,000 beers, wines, and spirits from local retailers in more than 70 cities across the United States and Canada. 

The company’s innovative approach to online drink delivery has made them a leader in the food delivery industry, and its focus on customer convenience has paid off. In 2021, the company was acquired by Uber, the largest ridesharing, food delivery, and transportation network company in the United States, for 1.1 billion USD. During the COVID-19 pandemic, the company’s sales grew 800% due to the enhanced demand to buy drinks with limited exposure to the virus. Of course, the idea behind Drizly was innovative and popular. However, their business model and use of data analytics made the company stand out in a competitive market with many other product delivery and e-commerce startups.

The graphic below highlights Drizly’s success timeline. Companies often take years and years until they reach the so-called “breakthrough,” if they’re lucky. As you can see, Drizly was able to achieve this in just nine short years, which further highlights the effectiveness of data analytics.

Business Model and Strategy

Drizly’s business model revolves around providing customers with a convenient, fast, and easy way to purchase drinks online. Unlike traditional retail stores or licensed premises, Drizly has no physical locations. Instead, they partner with local retailers who carry the necessary licenses to be able to provide delivery services. Customers can order from Drizly and deliver their orders to their homes, offices, or designated pickup locations. 

Drizly’s strategy is focused on customer convenience and providing a seamless online shopping experience. They offer a wide selection of beverages from local retailers, ensuring customers get the freshest products. The company also has a comprehensive delivery network, ensuring that orders are delivered quickly and efficiently. Additionally, Drizly offers a variety of promotional codes that customers can use to get discounts on orders. It’s no wonder that Uber was eager to make the acquisition. With the success Uber Eats, Uber’s food delivery service, has had, adding Drizly to its arsenal was a strategic business decision to maintain its position as the leading food and now beverage delivery company in the United States.

Drizly’s revenue model can be seen in the graphic below. As you’ll see in the next section, advanced analytics fuels this model.

Data Analytics

Data analytics has been a key part of Drizly’s success. Data has allowed them to create an effective and efficient supply chain, optimize prices based on customer demand, and ensure that they provide customers with the best possible products. 

Drizly consulted Hashpath, a data analytics consulting company, to speed up their time-to-market. Hashpath helped ease and speed processes like authentication and onboarding. The advantage of utilizing Hashpath’s services was that it ensured the longevity and long-term success of these processes for users and administrators. In the case of Drizly, we can see that it’s often advantageous to outsource the incorporation of data analytics to another firm. Startups should not feel intimidated by the need to adopt analytics-based tools. In today’s business landscape, data analytics is at the forefront of success. As seen in the graph below, the advanced analytics market is growing at a compound annual growth rate (CAGR) of 15%, which is very fast. Companies like Amazon and Meta are industry giants due to their use of advanced analytics. Furthermore, the jobs with the highest demand are all data-oriented positions. Therefore, outsourcing data analytics-based approaches to outside firms is an effective way startups can integrate these profitable techniques. 

While Hashpath’s services were extremely valuable to Drizly, the firm was not working alone. They consulted Google’s Looker, a data exploration and discovery company. Looker partnered with Hashpath to help Drizly create new revenue streams, including direct monetization. Drizly is able to take advantage of direct monetization to sell its customer reports to vendors. The company receives loads of Big Data about customers, which is a highly valuable asset to various companies. 

Another advantage of Big Data is to evaluate customer behavior and preferences, analyze sales data, and track delivery times. This analysis helps the company make informed decisions about how to expand its service offering, determine what products customers are looking for, and ensure that orders are delivered on time. 

In addition to using Big Data for tactical decision-making, Drizly also uses data insights to inform its marketing strategies. The company can quickly determine what types of customers respond best to certain promotions and tailor messages and offers to meet the needs of each customer segment. This level of personalization has been a key factor in the tremendous success that Drizly has seen over the past few years. 

Drizly uses data from customer orders to make informed decisions about product selection and pricing. This information allows them to stock the products customers are looking for and ensure they have competitive pricing. The company also uses customer feedback to make product and service improvements, which further boosts customer loyalty. 

Conclusion

Since its founding in 2012, Drizly has become the leader in online drink delivery due to its innovative approach to convenience and data analytics and customer-centric focus. The company has created an efficient delivery system and optimized pricing strategies by leveraging the services of Hashpath and Google’s Looker. It now stands as a major player in the food industry thanks to Uber’s acquisition of the firm in 2021. With its unique data-driven approach, Drizly will continue to grow and expand its operations in the future. 

Without question, Drizly has proven that adopting an analytics-driven approach is essential for startups to succeed in today’s crowded business landscape. Startups can benefit from the use of data analytics and should not be afraid to outsource these services to experienced firms. By utilizing advanced data tools, companies like Drizly are sure to remain at the top of their industry and maintain a competitive edge.  

References

https://cloud.google.com/customers/drizly

https://cloud.google.com/looker/

https://drizly.com/

https://www.contentstack.com/blog/all-about-headless/use-predictive-analytics-augmented-analytics-make-most-of-data/

https://www.apptunix.com/blog/drizly-business-model/

Arek will speak at the SwissCognitive World-Leading AI Network AI Conference focused on Beyond Efficiency: AI’s Creative Potential on 5th September.

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Copyright: imveurope.com – “Deep Learning Brings Optical Character Recognition Into Focus”

Ivar Keulers shares how new, innovative OCR tools are helping eliminate training time and ensure stability across a wide range of industrial use cases

Optical character recognition (OCR) isn’t a new technology. It’s been around since the 1960s and saw a resurgence since the 1990s. It’s a useful and needed tool for inspecting best-before-dates, serial numbers, lot numbers, and vehicle identification numbers (VINs) to ensure the correct components and parts are in the right place at the right time for the right model of vehicle.

However, the problems using OCR tools are also familiar. They need a lot of training time, can be unstable when faced with a change in environment, and don’t handle complex use cases well. Many OCR tools require manufacturers to invest a lot of time for something that is at best ‘okay’ and struggles to read obscure and damaged characters, engraved and embossed formats, characters on reflective and curved surfaces, or changing and harsh lighting conditions.

The challenges around much of today’s OCR are reflected in wider challenges with legacy machine vision systems. Setting up and managing industrial automation inside a manufacturing plant, for example, is often slow and difficult due to the reliance on multiple devices running different software with often old, antiquated user interfaces. The operational challenges around older machine vision systems also remain. These include hardware and software compatibility, financial costs, procurement times, maintenance, lack of interoperability, training and limitations when handling complex use cases.

Many vendors also utilise completely different software for their fixed industrial scanners and machine vision systems, which makes it all hard and costly for their customers to navigate. That runs counter to the core principles of scalability, longevity and compatibility that we apply across all portfolios, especially our mobility, scanning, and automation platforms.

Manufacturing industries have evolved. Production volume and speed keep going up, new safety and regulatory compliance measures must be met, the volume of data grows and needs to be sifted and turned into useful business insights. Manufacturers need modern machine vision solutions that can meet these challenges.

Enter AI-powered machine vision

Forward-thinking manufacturers are increasingly turning to the capabilities provided by artificial intelligence (AI), specifically a subset of machine learning called deep learning, in their machine vision applications. A recent global survey^[1] of original equipment manufacturers in the automotive industry found that 24% are using machine vision today, with 44% planning to use it by 2027. That’s a significant 83% increase. A 70% jump was also seen around current use (27%) and future use (46%) of machine learning.

The benefits of machine vision are clearly seen in industries that require higher levels of safety, quality, compliance and efficiency at speed, including automotive, food and beverage, pharmaceutical, and electronic manufacturing.[…]

Read more: www.imveurope.com

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Copyright: news.mit.edu – “An AI challenge only humans can solve” – AI and Automation

The Dark Ages were not entirely dark. Advances in agriculture and building technology increased Medieval wealth and led to a wave of cathedral construction in Europe. However, it was a time of profound inequality. Elites captured virtually all economic gains. In Britain, as Canterbury Cathedral soared upward, peasants had no net increase in wealth between 1100 and 1300. Life expectancy hovered around 25 years. Chronic malnutrition was rampant.

“We’ve been struggling to share prosperity for a long time,” says MIT Professor Simon Johnson. “Every cathedral that your parents dragged you to see in Europe is a symbol of despair and expropriation, made possible by higher productivity.”

At a glance, this might not seem relevant to life in 2023. But Johnson and his MIT colleague Daron Acemoglu, both economists, think it is. Technology drives economic progress. As innovations take hold, one perpetual question is: Who benefits?

This applies, the scholars believe, to automation and artificial intelligence, which is the focus of a new book by Acemoglu and Johnson, “Power and Progress: Our 1000-Year Struggle Over Technology and Prosperity,” published this week by PublicAffairs. In it, they examine who reaped the rewards from past innovations and who may gain from AI today, economically and politically.

“The book is about the choices we make with technology,” Johnson says. “That’s a very MIT type of theme. But a lot of people feel technology just descends on you, and you have to live with it.”

AI could develop as a beneficial force, Johnson says. However, he adds, “Many algorithms are being designed to try to replace humans as much as possible. We think that’s entirely wrong. The way we make progress with technology is by making machines useful to people, not displacing them. In the past we have had automation, but with new tasks for people to do and sufficient countervailing power in society.”

Today, AI is a tool of social control for some governments that also creates riches for a small number of people, according to Acemoglu and Johnson. “The current path of AI is neither good for the economy nor for democracy, and these two problems, unfortunately, reinforce each other,” they write.

A return to shared prosperity?

Acemoglu and Johnson have collaborated before; in the early 2000s, with political scientist James Robinson, they produced influential papers about politics and economic progress. Acemoglu, an Institute Professor at MIT, also co-authored with Robinson the books “Why Nations Fail” (2012), about political institutions and growth, and “The Narrow Corridor” (2019), which casts liberty as the never-assured outcome of social struggle.[…]

Read more: www.news.mit.edu

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Der Beitrag David Wood erschien zuerst auf SwissCognitive | AI Ventures, Advisory & Research.

Copyright: htworld.co.uk – “29 health tech innovations which are truly changing healthcare”

1- Intuition Robotics | Israel

Intuition Robotics is on a mission to empower older adults to live happier, healthier and more independent lives at home.

The company’s award-winning product, ElliQ, is a proactive care companion for older adults – which is significantly improving healthcare for seniors ageing in place and dealing with the loneliness crisis.

Loneliness has been an epidemic for years, and it’s been exacerbated by social isolation brought on by the COVID-19 pandemic.

ElliQ Intuition Robotics has won several awards for its work with ElliQ including Fast Company’s Most Innovative Companies and the CES Best of Innovation award.

The company was founded in 2016 and is based in Tel Aviv with offices in San Francisco and Athens.

Intuition Robotics’ investors include: Toyota Ventures, Samsung NEXT, iRobot, OurCrowd, Terra Ventures and Venture Capital firms from California, Israel, Japan, and Asia.

• CEO: Dor Skuler
• Explore Intuition Robotics

2 – NovaXS | USA

NovaXS is a smart medical device company developing a needle-free injection device that syncs with a smartphone app to make the treatment process easier and pain-free for those suffering from chronic conditions and diseases.

One in four adults and two in three children have a fear of needles, only 50% of medications for chronic disease are taken as prescribed and the error rate for administering medications at home is up to 33%.

This all adds up to higher healthcare costs and results in negative health outcomes for patients that suffer from common illnesses like diabetes, growth hormone deficiencies, allergies and more.

NovaXS Biotech is on a mission to make medication self-administration as easy as making morning coffee.

The prominent medical device startup is focused on advanced drug delivery and users’ long-term health. Its patent-pending technology is a needle-free drug delivery platform. It allows patients to self-administer biologics subcutaneously or intramuscularly and track long-term treatment progress through IoT integration and software app.

• Founder: Alina Su
• Explore NovaXS

3 – Oxford Longevity Project | Oxford, UK

The Oxford Longevity Project aims to make the latest scientific breakthroughs in longevity accessible to the general public. It is a team of scientists and doctors uniting to change how we understand healthcare for good.

Together, they’ve capitalised on the public’s increased comfort with virtual learning and conferences by holding quarterly global webinars via main online platforms.

What makes OLP unique is their commitment to connect influential clinical practitioners and leading scientists on the same topic.

The speakers have two aims: First and foremost to translate the latest science into accessible information that patients and other interested non-scientists can action themselves; and, second, to more quickly connect doctors to the latest protocols.

Many researchers believe that it takes up to 17 years to translate breakthrough bench research into protocols used in clinics and OLP hope to change this by forging these connections.

One of the scientific discoveries that inhibits ageing are autophagy or cellular renewal, recycling and repair and this led to their previous webcasts ‘Autophagy and Alzheimers’ and ‘Autophagy and Ageing.’ to discuss this topic.

They’re committed to offering free information and empowering people to pursue health and living longer on their own terms, with accessible language, live seminars and free catch-up videos-all at no cost.

• Founders: Leslie Kenny & Oxford University Emeritus Professor Denis Noble
• Explore Oxford Longevity Project […]

Read more: www.htworld.co.uk

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Copyright: venturebeat.com – “How data and automation can help with sustainability”

Why sustainability makes sense

For years, companies of all sizes have recognized the inherent value of environmental, social and governance (ESG) initiatives when it comes to customer retention and smooth overall operations. Sustainability strategies are a smart business move that may foster company longevity and keep customers coming back.

However, while plenty of company leaders recognize the critical importance of sustainable initiatives, only about one-fourth of companies include sustainability as part of their business model, according to the International Institute for Management Development (IMD). For the greatest chance of long-term business success, the Switzerland-based organization encourages executives and company policymakers to first comply with local laws and regulations and then take a more proactive approach to sustainability.

To that end, data and automation can help, by giving established companies and startups alike the necessary tools to meet their sustainability goals.

Breaking barriers and implementing green initiatives

Ideally, a company’s sustainable initiatives should be authentic and environmentally focused, rather than rooted in the hope of increased profits. Today’s tech-savvy consumers increasingly use their spending power to support environmentally conscious companies and are even willing to shell out a few extra dollars on sustainable products and brands. Forward-thinking companies can maintain transparency by disclosing their sustainability goals and initiatives publicly and by encouraging customer feedback.

Yet, that feedback won’t amount to much without the ability to make sense of it all, and automation can be a game-changer in this regard. Automation software can help alleviate some of the burdens of data interpretation, enabling companies to speed up their green initiatives and saving time and money. For example, with automation software on hand, companies can quickly and easily track energy usage, amount of waste produced daily, consumer habits, carbon footprint and more, in an effort to streamline operations. […]

Read more: www.venturebeat.com

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copyright by www.designnews.com
As the demand for next-generation batteries grows, scientists are turning to new techniques to aid in the design of safer and more reliable batteries before fabrication of them even begins.

Among them are researchers from the United Kingdom’s Cambridge and Newcastle Universities, who have designed a new machine-learning method that they said can predict battery health with 10 times more accuracy than the current industry standard. The method, which monitors batteries by sending electrical pulses into them and measuring the response, could be used to develop safer and more reliable batteries for electric vehicles (EVs) and consumer electronics, said Alpha Lee from Cambridge’s Cavendish Laboratory, who co-led the research.

Indeed, one of the key issues with lithium-ion batteries commonly used in devices like mobile phones is that battery performance degrades over time for various reasons stemming from chemical processes. This continues to limit the widespread use of EVs, which also use lithium-ion batteries.

While individually, one of these chemical processes may not do enough damage to affect performance, combined they can limit a battery’s performance and shorten its lifespan, which is why researchers are seeking answers to the mysteries of battery degradation, Lee said.

“Safety and reliability are the most important design criteria as we develop batteries that can pack a lot of energy in a small space,” he said in a press statement. “By improving the software that monitors charging and discharging, and using data-driven software to control the charging process, I believe we can power a big improvement in battery performance.”

Add-On Battery-Monitoring System

Currently, battery health is predicted by tracking the current and voltage during battery charging and discharging. However, this type of monitoring does not take into consideration the process happening within the battery, which require new ways to measure batteries while they are in action—such as algorithms that can detect subtle signals as they are charged and discharged.

The method Lee and his colleagues developed combines a monitoring system of electrical pulses and machine learning. Researchers send electrical pulses into the battery to measure its response, then use a machine-learning model to pinpoint aspects of the electrical response that show that the battery is aging, said Yunwei Zhang, another researcher at the Cavendish Laboratory who also worked on the project.

“Machine learning complements and augments physical understanding,” he said in a press statement. “The interpretable signals identified by our machine learning model are a starting point for future theoretical and experimental studies.”

Researchers published a paper on their findings in the journal Nature Communications.

The team performed more than 20,000 experimental measurements to train the model, which researchers said can learn how to distinguish important signals from mere noise. Moreover, the system can be used with existing batteries as an add-on rather to measure their health, they said. […]

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The company has attracted millions of dollars from strategic investors and uses novel techniques to engage customers.

The doc.ai platform uses AI to help people enroll in clinical trials, determine the healthiest places they have lived and eventually see what their genetic data can tell them about health and longevity. The startup has a major deal with Anthem, Inc. the nation’s second-largest health insurer, operating Blue Cross and Blue Shield plans in 14 states. Together they are working with Harvard to enable consumer participation in studies best suited to their personal profiles.

Founder and CEO Walter De Brouwer is one of Silicon Valley’s new breed of data entrepreneurs whose goal is to engage consumers about understanding their health information. At a time when technology giants are expanding their reach into data mining by partnering with providers of electronic medical records (EMRs), some start-ups are using a different approach. These young companies are focusing on empowering individuals and are catching the attention of the insurance industry.

The AI health data mining market is expected to exceed $6 billion by 2021 and has been dominated by large companies along with some notable failures. It was recently reported that Amazon will sell software to hospitals and health systems to mine patient data so they can improve treatments and cut costs. Google Health was shuttered when its personal online records failed to attract users but the company is jumping back into the waters with its Google Cloud Healthcare Service geared towards big clients.

The health insurance industry is helping driving adoption of AI, a market growing 35% annually according to Accenture. Insurers have long used sophisticated analytical methods to manage costs, and now are turning to AI to help stay close to patients. UnitedHealth Group’s Optum is an investor in Buoy Health, a company started out of Harvard’s Innovation Lab and offers patients an AI-powered digital health assistant.[…]

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