Technology then and now in Healthcare.

The pandemic has been the best accelerator of innovative technological implementations in the healthcare industry. Rightfully, the healthcare industry is now thriving on modern technological developments.

Few high-end technologies that are now the crucial slices of a healthcare unit are beacon gadgets for crowd management while machine learning devices are deployed to yield appropriate disease detection and treatment methodologies. Such adoption of innovative technologies has helped medical professionals to devote more time to value-added activities like strategizing on the enhancement of patient care.

Technology will Shape the Future of Healthcare

Technology, in healthcare, is progressing along with transforming medical conducts as a whole. Such a phenomenon has struck health experts with a fresh perspective that can be leveraged for improving the healthcare profession.

Precisely, technology innovation is shaping the healthcare industry, making it sustainable. Technology in healthcare will generate a cost-effective and efficient health infrastructure.

A Glimpse of the Current Medical Landscape of India

The past five years have been very crucial and significant for the transformation of the healthcare sector. The transformation involves upgrading diagnostic tools, ventilators, medical, and surgical devices. Such transformations have resulted in positive outcomes.

A relevant example of technological transformation is ATMAN AI. ATMAN AI is an AI-powered tool used to detect complicated side effects of Covid-19 and heart diseases. This development was aimed at rural residents and residents living on the outskirts. Such a development meticulously contributed to reshaping the health infrastructure in socially backward areas, ensuring health equity for all.

Additionally, specialized surgical innovation that is inclusive of high-end technology and research has also evolved over the last two decades.

What Does Technological Transformation Mean for Healthcare System in India?

The healthcare system in India is often doubted and brought under question. The primary reason of which can be lack of skilled medical professionals and overwhelm of traditional medical conducts. There are about 500 surgeons who are adept in robotic-assisted surgeries (RAS).

Economists have pointed out the need to speed up technology adoption by the healthcare systems in India. Increased adoption of technology and proper training of the human workforce to operate disruptive technology incorporations can elevate the status of the Indian healthcare system, giving rise to more equity in healthcare opportunity dissemination.

Technological Adoption: Quick Stats

In 2021 around 8 hospitals out of 10 from all across the globe have faith in results yielded by AI-powered tools. Around 76% of healthcare professionals in India use machine learning tools and gadgets that are driven digitally.

The post TECHNOLOGICAL INNOVATIONS WILL BE THE CORNERSTONES OF HEALTHCARE appeared first on Artificial Intelligence.

Telecommunications might not be the first sector that comes to mind when we think of industries that are considered the pioneers in customer experience. During COVID-19, the expectations for customer experience are even more profound and the stress on telecom providers couldn’t be higher. While we would not have been able to maintain our ability to work, stay socially connected, learn, shop, get healthcare, and more during the pandemic without the connectivity that telecom companies deliver, the current customer experience has tremendous potential for transformation.

Despite the growing global dependency on reliable and fast connectivity, downward trends that have impacted telecom companies for years continue to affect their present performance and hinder their ability to emerge stronger after the crisis. Slow growth due to declining ARPU (average revenue per user), industry saturation, customer churn, competitive disruptions, and the need to achieve return on investment into 5G and other technologies all continue to impact the industry.

According to Bain & Company, COVID-19 has caused a rapid and sharp reduction in new customer sign-ups. Telecoms need to transform customer experience to reverse the long-term trends of poor customer satisfaction, low loyalty scores, and high churn rates.

Telecom players are laggards in adoption of emerging technologies
Many telecommunication service providers are laggards compared to other industries when it comes to customer experience. High call volumes generated during the pandemic only served to emphasize the state of the archaic systems and processes in their contact centers. Long wait times, slow resolutions, and the challenges of remote work combined has frustrated both customers and agents.

Nearly 80 percent of people polled in a survey conducted by a leading global conversational AI company in April 2020 want to speak with an agent. Yet over 33 percent of callers waited for more than 30 minutes on hold, with 5 percent saying they waited for more than 2 hours in the same survey.

We all know customer tolerance has its limits; 72 percent of the survey respondents say they ended a call while still waiting to speak with a live person, and 43% of respondents who did get through to an agent said they felt angry, annoyed, or frustrated because they were put on hold.

AI and automation can remove friction
Artificial intelligence (AI) and automation can provide telecom companies with a way to move from a laggard position to a place among leaders in customer experience. Companies must use these technologies to deliver a frictionless customer experience anywhere along with an end-to-end conversation—from self-service channels (mobile, web, and IVR) to live agent interactions.

Boston Consulting Group(BCG) believes that AI technologies will “accelerate the rethinking of customer interaction to improve customer experience.” Citing examples of AI usage within telecom companies, BCG reports that “At a challenging time for customer service staff and field forces, AI has helped decrease the time employees spend on simple tasks and refocused them on more pressing issues.”

While some telecoms have deployed point AI or automation solutions, they have not been successful in creating the much-needed frictionless, end-to-end experience for customers. For example, many self-service channels still do not understand customer sentiments or intent, nor transfer this context as conversations move between channels. Agents have limited in-call coaching guidance or lack immediate access to customer information, such as customers’ current equipment, which increases average handle times and creates delays in order fulfillment. That is because point solutions do not cover an entire conversation and instead create additional silos within the customer journey.
A Conversational Service Automation (CSA) platform in comparison uses conversational AI, robotic process automation (RPA), and workflow automation to bridge humans and machines and back office and front office operations throughout a customer conversation and across digital and voice channels.

Customer-centric telecom experiences
Telecoms can deploy CSA to meet the new needs of customers with empathy and understanding. It can help agents be more effective and productive irrespective if they are working from home or in the contact center and still deliver a high-quality experience at lower costs. Telecoms can remove the friction that frustrates their customers by using CSA to automate and transform their customers journeys. Here’s how:
Conversational Self-Service: Automate telecom journeys such as paying bills, service inquiry, service upgradation, tracking usage, and more. Conversational AI can help telecoms understand customer sentiments and intent to drive self-service automation rates, thus reducing contact center costs. Should a customer need agent assistance, a CSA platform passes context from self-service to agents to create a frictionless interaction and a lower average handle time.

Agent Co-Pilot: CSA enables real-time analysis of customer context, including sentiment and intent, to help co-pilot agent performance with in-call coaching alerts. The platform gives agents insight to be more empathetic as well as to improve upselling or cross-selling. For example, the AI could suggest that the agent offers a triple or quadruple play bundle when a customer already has more than one service; this helps increase ARPU.

Post-Call Analytics: A CSA platform can automate post-call analytics on 100% of customer interactions. These include voice, email, and chat to understand reasons for customer churn and sales effectiveness. It also helps drive 100% compliance and identifies other core, customer, and agent-centric experience areas for planning and operational improvements. Telecoms can also get insights into how customers are using channels such as self-service as well as to identify potential points of friction in a customer journey to optimize customer experience.

Build stronger, lasting relationships
As consumers navigate their way through these challenging times, understanding and acting on customer conversations will be critical for telecoms to drive measurable and sustainable improvement in customer satisfaction and loyalty. Automation helps telecoms deliver the kind of frictionless experiences that consumers today demand while reducing operational costs.

Automating and improving the entire conversation—self-service, human-to-human, and after a call—using CSA is a winning way for telecoms to overcome immediate challenges and emerge more robust in a post-COVID world.

The post Transforming the telecom industry with Artificial Intelligence appeared first on Artificial Intelligence.

Around 100 engineers work at the location to develop complex product solutions for electric drive systems, actuators, and electric auxiliaries for vehicles with hybrid, hydrogen, or battery electric drives. MAHLE has invested mid-double-digit millions to set up the centre. At the start of the year, MAHLE founded a dedicated business unit for electronics and mechatronics. The technology group expects sales growth in this business segment to be well into double digits in the next 10 years.

“Our new mechatronics development centre is another important step in MAHLE’s technological transformation,” says Wilhelm Emperhoff, Member of the MAHLE Management Board and responsible for the Electronics and Mechatronics business unit. “It’s now particularly important that we develop the right know-how and portfolio so that we can emerge from the current crisis in a stronger position as an established technology and development partner in the automotive industry.” 

The new development facility in Kornwestheim joins MAHLE’s most important electronics and mechatronics development locations worldwide. At centres in Sempeter pri Gorici/Slovenia, Valencia/Spain, and Changshu/China, its engineers are working hard to drive forward the company’s research and development activities in this field.

Product innovations from Kornwestheim
The priority areas for the new development centre are mechatronics, electrics/electronics, and the programming and software needed for complex product solutions and systems. “Our work here will particularly focus on the development of complete systems. This is an area in which we have a lot of experience—experience that we can combine with our expertise in electric actuators and electronic controls to achieve optimum results,” says Dr. Armin Messerer, head of the global Mechatronics business segment. 

One example of a product solution in this field that has recently been developed at the location is the electric expansion valve (EXV). The EXV is used in refrigerant circuits to ensure that the air-conditioning system and the cooling of the traction batteries is optimally controlled. These components will go into series production starting in 2022 and can be used in vehicles with conventional combustion engines as well as in battery electric or hybrid applications. Products like this expansion valve can be comprehensively tested and validated in state-of-the-art test facilities, such as those now also located in Kornwestheim.

In its new technology centre, MAHLE has created modern office areas with open-plan workplaces, co-working and meeting areas, and redesigned social spaces. “The working environment reflects the latest findings in terms of safety at work and workplace design. It’s modern and flexible, and intended to encourage communication and creativity,” explains Messerer.

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Warren Buffett is notorious for guiding his massive conglomerate, Berkshire Hathaway (BRK.A) (BRK.B), by the core tenets of value investing. Under his leadership, the business has a long history of buying excellent companies at a fair price, but things have not always been this way. Originally, Buffett’s ideology was more along the cigar-butt style of investing proposed by Benjamin Graham. This involved buying fair or even poor companies at a wonderful price and taking one last ‘puff’ from them before they died. As his conglomerate grew and the influence of partner Charlie Munger rubbed off on him, his strategy shifted to what made Berkshire the behemoth it is today. Now, it looks like times are changing again.

In the new era, there are few excellent companies that can be bought at a fair price that are large enough to move the needle for the Oracle of Omaha. With the continued shift of our society toward technological innovation and the influence of younger managers like Ted Weschler and Todd Combs, Berkshire’s approach has been changing. The latest example now is Berkshire’s decision to invest hundreds of millions of dollars into the IPO of Snowflake (SNOW). Weschler and Combs have been largely responsible for smaller investments around this size, but Buffett has not been absent from decisions on the tech side. Probably the greatest example of Buffett’s commitment to this change was his willingness to allow Berkshire’s stake in Apple (AAPL) to grow to more than $100 billion (including appreciation of the stock in the business) as of August of this year. Because of the size of the deal, it’s unclear who all is responsible for the decision to participate in Snowflake’s IPO, but it could be a sign that investors should consider following suit if they can buy in at a price similar to what Berkshire is receiving in the transaction.

A look at the move

Though Buffett has not always stayed away from IPOs, investing in them has been a rarity. His view has been that they are generally overpriced. In this case, however, Berkshire has decided to throw caution to the wind. According to the terms of the agreement between Snowflake and Berkshire, the latter will acquire $250 million worth of Class A common units in the IPO. They are joined by a Salesforce (CRM) subsidiary, which is making a transaction of identical size. This will be new money injected into Snowflake, and unlike the rest of the company’s IPO, it will not carry underwriting fees and discounts. If Snowflake goes public at $80 per unit, which is the mid-point of its expected range, this would translate to Berkshire receiving 3.125 million common shares in the business. In addition, Berkshire has agreed to buy 4.042 million Class A common units from existing shareholders in the business at the IPO price. Collectively, this could result in a price to Berkshire of $573.36 million with the $250 million commitment factored in and assuming the $80 per share estimate.

This maneuver by Berkshire is part of a larger offering by Snowflake. The company is issuing 28 million Class A common units, plus they are making another 4.20 million available for their underwriters. In all, if the IPO price is $80 per share, the company would receive net proceeds of between $2.7 billion and $3 billion. That’s inclusive of the combined $500 million that Berkshire and Salesforce are allocating toward it. With everything said and done, and without factoring other possible share issuances that could be made for employee compensation and other things, Snowflake should end up with 278.78 million common units available, valuing the firm at around $22.30 billion. All incoming investors are receiving Class A common shares, while insiders are receiving Class B ones. Economically speaking, these units are identical. However, the Class A units carry just one vote apiece, while the Class B units carry 10. This gives the insiders of the business an overwhelming majority of the vote over the business’ affairs.

An interesting prospect to consider

Until the dust settles, it’s impossible to know where the market will value Snowflake. Having said that, investors should consider picking up some units in the business. Fundamentally, this may seem odd, since the business historically has only lost money and is certainly overvalued with such a large market cap as what is projected. Having said that, its business model is sound and its growth is phenomenal. On the business side, it may be worth mentioning what exactly the firm does.

According to management, Snowflake, at its core, is the provider of what it calls its Cloud Data Platform. This platform hosts the Data Cloud, which is an ecosystem that allows customers to break down data, analyze it through innovative data sets, and derive value from the results of their analysis. One example video shows a bike-sharing firm looking at user behavior data and comparing it with independent weather data to determine the cause of slow times of the year. This is a gross understatement of all that the business does, but the best way to view the firm is through the lens of its layers. The first of these is a storage layer that collects and unifies structured and semi-structured data. The second layer is where users compute that data using common data sets. The third includes the firm’s cloud services, which help to optimize a user’s use cases.

Recently, customers have come to realize the potential that Snowflake has toward optimizing their data. Consider, for instance, the firm’s financial performance. In the first two quarters of its most recent fiscal year, revenue has come out to $241.96 million. This is more than double the $104.04 million seen the same time last year. In the fiscal year ending in January of 2019, sales were just $96.67 million. They have surged in the latest completed fiscal year to $264.75 million. As you can see in the image above, Snowflake’s profitability has been a concern, but in the first two quarters of the current fiscal year, it saw operating cash outflows total just $45.27 million. This compares favorably to the $110.02 million seen a year earlier.

What has caused Snowflake’s growth is something really simple: a surging customer count. At the end of its latest fiscal year, the firm had 2,392 clients. This is up from 948 a year earlier. Total customer count today is closer to 3,117, up from 1,547 a year ago. In the month of July this year, the larger user base has a significant impact on Snowflake’s activity. The firm reported 507 million daily queries during the month. This is nearly double the 254 million seen in July of 2019. Such rapid growth combined with how quickly the company is approaching operating cash flow breakeven is encouraging.

Takeaway

Right now, Snowflake represents an interesting prospect. What’s more, it’s a firm that has the rare distinction of being both tech-oriented and that was vetted by Berkshire. As the world becomes more digital in nature, data will hold ever-greater importance in our lives and Berkshire and Salesforce probably both see this as a play on the growth of data in the global economy. Are shares pricey for where the company is fundamentally? Without a doubt. But is it likely to experience tremendous upside growth in the years to come? I would say yes.

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Emerging from dire straits, the all-female Afghan Robotics Team aims to change how people think about their war-ravaged country by back-to-back low-cost technological inventions for public welfare.

Hailing from Afghanistan’s historic western city of Herat – the epicentre of Coronavirus pandemic – the all-girl team have won many hearts with their latest invention of an affordable open-source, low-cost ventilator at a time when Coronavirus cases were on an upward trajectory.

In an exclusive interview with FairPlanet, captain of the team Sumaya Faruqi said the young and promising students have much more to offer and have much bigger dreams to follow.

“We faced so many challenges in producing the vantilator. Of course there was a shortage of resources, and then there were restrictions on movement due to lockdown in connection with the Coronavirus, but we never lost hope, and never thought about quitting”, the determined young leader said.

As she explained, the group of eight girls under the supervision of three teachers pulled together this low-cost ventilator by using a motor of an old car, some 3D printed parts and other easily available tools to address the pressing need of Coronavirus patients in a country with fragile public health system.

“It can fully replace a branded and imported ventilator, which costs a lot. It properly controls the flow of oxygen, the volume of it in connection with the recommendations of the doctors for patients of different age and conditions”, she explained.

Health authorities in Afghanistan confirmed this month that at least ten million people have been infected with Coronavirus in the country. Sharing details of this nationwide survey, health minister Ahmad Jawad Usmani told a news conference on August 5th that COVID-19 has infected at least 31.5 per cent of the whole population of the war-ravaged country.

This came as the Afghan government has approved reopening of universities across the country. The public offices in the capital Kabul and in all provinces also began working with normal routine while schools across the country still remain closed. Afghanistan woke up to face the grim challenge posed by COVID-19 at least three months after the virus was first detected in China in December 2019. The threat came knocking on the country’s western border with Iran when thousands of panicked Afghan refugees began returning forcefully as well as voluntarily with evident signs of the virus on a daily basis.

The rise to fame

Members of the girls’ robotics team first rose to fame by winning the silver medal for “courageous achievement” at the First Global International Robotics Challenge in the U.S. last year. Their latest invention at home has earned them more respect and recognition from the general public. However, it is yet to receive full recognition from the authorities.

“There has been encouragement and plenty of kind words for us on social media…We are now working on a number of other low-cost technological ideas as well. One of these ideas is a UVC anti-infecting lamp for covered places, such as hospitals”, team captain Sumaya explained.

Their journey to fame was never easy. When the world media was praising them for winning the Entrepreneurial Challenge at Robotex (described as the “Europe’s biggest robotics festival”) in the Estonian capital of Tallinn two years ago, there was little mention of it in their home country.

Two years ago, members of the team were in tears when, after initial rejection of a visa, the group was finally allowed to fly to the U.S. to represent their war-torn country among 157 countries for a science fair at the Constitution Hall in Washington.

The team spokesperson, Diyana Wahabzada, said looking forward, the girls are now working on concepts to address some pressing issues. “Every year, we lose so many of our countrymen and women to landmines”, she said while hinting that the all-female robotics team aims to pull together a low-cost invention to clear these deadly explosive devices as well. “A prototype mine-cleaner, which helps operator complete this task from a safe distance is almost ready”, she said.

Latest figures by the Afghanistan Independent Human Rights Commission suggest that in the first six months of 2020, a total of 2,957 civilians have been killed or injured in 880 different security incidents (1,213 killed, 1744 injured). Landmines continue to be among the leading factors behind these tragic killings.

The inspiring girls of the all-female robotics team want all this changed for good. “I want to see in my lifetime Afghanistan known for science and technology where boys and girls can nurture their talent for the welfare of their country and the entire world”, Sumaya said.

The post How The All-Female Afghan Robotics Team Is Changing Perceptions appeared first on Artificial Intelligence.

Global Coronavirus pandemic has impacted all industries across the globe, Dining Chairs market being no exception. As Global economy heads towards major recession post 2009 crisis, Cognitive Market Research has published a recent study which meticulously studies impact of this crisis on Global Dining Chairs market and suggests possible measures to curtail them. This press release is a snapshot of research study and further information can be gathered by accessing complete report.

The market research report covers the situations for growth, recovery and long-lasting structural impact from the unfolding crisis for the Dining Chairs market. The report also offers the most recent and efficient analysis of the Dining Chairs market, along with, top manufacturers, regions, applications, types right from 2020 to 2027. The Global Dining Chairs market research report 2020 also covers summary of consumer’s wants and needs.

Manufacturers are facing continued downward pressure on demand, production and revenues as the COVID-19 pandemic strengthens.manufacturers should be prepared for major global supply chain disruptions. Thus, some of the key players are mainly focusing on research & development to provide inovative products to clint. The report provides an in-depth analysis regarding key players of the market along with their company profiles that contains all the information of its happening and recent developments. Various factors influencing the market development and growth as well as latest development strategies implemented by key players that can influence the growth of the market is explained. Graco, Evenflo, Stokke, Fisher Price, Cosco, Chicco, Peg Perego, Phil & teds, Mamas & Papas, Joovy, Maxi Cosi, BabyBjorn, Badger Basket, Combi, Hauck, Bloom, Inglesina, AdCraft, Summer Infant, The First Years, Beijing Aing, Cixi Babyhome Products, Aricare are some of the key players operating in this market.

Global Dining Chairs Market Segmentation by Regions:
• North America (U.S., Canada, Mexico)
• South America (Cuba, Brazil, Argentina, and many others.)
• Europe (Germany, U.K., France, Italy, Russia, Spain, etc.)
• Asia (China, India, Russia, and many other Asian nations.)
• Pacific region (Indonesia, Japan, and many other Pacific nations.)
• Middle East & Africa (Saudi Arabia, South Africa, and many others.)

The COVID-19 pandemic has troubled the economic, political, financial, social and religious structures of the whole world. The governments have forced lockdown which has stopped the tasks and procedures of firms and various industries. The outbreak of Covid-19 is challenging governments to implement monetary and fiscal policies that will help to support the market and also sustain economic activity. The pandemic have impacted scarce in crude materials and shortage of labor, which also restrict the growth of the global Dining Chairs market in the forthcoming years.

Report provides various market information like costs of several merchandises within the market, data of the offers and demand scenarios. The report also cover Dining Chairs market’s wider role which is known by serving to its shoppers to know social, technical and even legal aspects of markets. In market trends the report covers the upward or downward movement of a market. The Dining Chairs Market covers all global key players in the competitive market. The research report provides segmentation information based on types, region, application, and division of the market, population into subgroups, geographic variations, demographic variations and technographic variations.

The COVID-19 pandemic has troubled the economic, political, financial, social and religious structures of the whole world. The governments have forced lockdown which has stopped the tasks and procedures of firms and various industries. The outbreak of Covid-19 is challenging governments to implement monetary and fiscal policies that will help to support the market and also sustain economic activity. The pandemic have impacted scarce in crude materials and shortage of labor, which also restrict the growth of the global Dining Chairs market in the forthcoming years.

Report provides various market information like costs of several merchandises within the market, data of the offers and demand scenarios. The report also cover Dining Chairs market’s wider role which is known by serving to its shoppers to know social, technical and even legal aspects of markets. In market trends the report covers the upward or downward movement of a market. The Dining Chairs Market covers all global key players in the competitive market. The research report provides segmentation information based on types, region, application, and division of the market, population into subgroups, geographic variations, demographic variations and technographic variations.

Diverse parts of COVID-19 Impact on Global Dining Chairs market report:
The research report enrolls substantial data about the market share of each product portion and benefit valuation. The Dining Chairs market research report also covers assessed development rate, anticipated item request, and market share of every application.

The examination of the major value patterns and the possible factors that are driving market development is also mentioned in the report. The market report offers with cost structure of the makers, downstream purchasers, and distributors.

Influence of the Global Community Development And Regulation Application Market Report:
• Comprehensive assessment of all possibilities and risks in the Dining Chairs Market.
• Global Dining Chairs Market recent additions and major events
• A detailed study of business policies for the growth of the Dining Chairs Market-leading players
• Definitive study about the growth land of the Global Dining Chairs Market for forthcoming years
• The in-depth belief of Dining Chairs -particular drivers, constraints, and major micro markets
• Favorable impression inside requisite technological and market latest trends hitting the Dining Chairs Market

There are 6: Chapters to display the Global Dining Chairs Market:
Chapter 1: Market Overview, Drivers, Restraints and Opportunities, Segmentation Overview
Chapter 2: Market Competition by Manufacturers
Chapter 3: Production, By Types, Revenue and Market share by Types
Chapter 4: Consumption, By Applications, Market share (%) and Growth Rate by Applications
Chapter 5: Market Forecast
Chapter 6: Dining Chairs Research Findings and Conclusion, Appendix, methodology and data source

Cognitive Market Research is one of the finest and most efficient Market Research and Consulting firm. The company strives to provide research studies which include syndicate research, customized research, round the clock assistance service, monthly subscription services, and consulting services to our clients. We focus on making sure that based on our reports, our clients are enabled to make most vital business decisions in easiest and yet effective way. Hence, we are committed to delivering them outcomes from market intelligence studies which are based on relevant and fact-based research across the global market.

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Florida Atlantic University recently announced that the Dorothy F. Schmidt College of Arts and Letters, in collaboration with FAU’s Brain Institute, hired Susan Schneider, Ph.D., as William F. Dietrich Chair in Philosophy.

Dr. Schneider will bring her experience in analyzing the moral and ethical issues related to artificial intelligence and machine consciousness to FAU’s research in AI, brain and mind.

“Dr. Schneider’s work on the future of human intelligence is leading international conversations on human cognition, the future of the mind, and its relationship to the ever-growing presence of artificial intelligence in our society,” Michael J. Horswell, dean of the Dorothy F. Schmidt College of Arts and Letters, said in a statement. “Her research reminds us of how vital philosophy is to scientific advancements, and how the humanities must be in dialogue with science and engineering as we imagine and create the future.”

Dr. Schneider comes to FAU from the University of Connecticut, where she was a professor of philosophy and cognitive science and the director of the AI, Mind and Society Research Group. She also is the NASA-Baruch S. Blumburg Chair and a Distinguished Scholar Chair at the Library of Congress.

Her most recent book (2019), “Artificial You, AI and the Future of Your Mind,” considers the possibilities, definitions and moral/ethical issues related to machine consciousness.

“Dr. Schneider’s scholarship, and passion for student and community engagement, will accelerate FAU’s rise as a premier center for research and education into the mind’s origins and future,” Randy Blakely, Ph.D., director of FAU’s Brain Institute, said in the statement.

Dr. Schneider’s work in artificial intelligence has taken her to Washington, D.C., to testify before Congress on topics like data privacy, technological unemployment, autonomous weapons and more.

She also is a recipient of the National Endowment for the Humanities Public Scholar Award.

“I’m eager to help facilitate interdisciplinary connections between AI, neuroscience, philosophy and psychology,” Dr. Schneider said in the statement. “I’m currently writing a book about the future of intelligent systems (including human intelligence augmentation), so my interests range from deep learning systems to the octopus. I can’t think of a better place to engage with scholars on these issues.” ¦

The post FAU hires philosopher and futurist appeared first on Artificial Intelligence.

A migration to microservices is typically driven by flexibility, productivity and scalability demands. However, just because enterprises want these benefits, it doesn’t mean that their business, or IT teams, are ready for the change.

Get advice in the five articles featured here to determine if microservices adoption is right for your project, learn how to get teams ready for such an architecture, plan and execute the migration, and avoid common pitfalls.

Is your team ready to migrate to microservices?

While many have benefitted from a microservices architecture, that doesn’t mean it is a perfect fit everywhere. Two factors come into play: whether an application will succeed divided into microservices, and if the enterprise software team possesses abilities to create and manage them.

Teams face operation complexities when they go through a migration to microservices. Determine their readiness for the new architecture by evaluating standards for code reviews, CI/CD use, testing practices and other aspects of the code delivery workflow.

Joydip Kanjilal, software architect, proposes investment in these areas to tackle a microservices architecture: componentization, decentralized data management, infrastructure automation and resilient fault tolerance.

Flag any team processes that will not work with microservices. Consider restructuring Agile roles and responsibilities that focus on monolithic application development to work in the new setup.

Who does what on a microservices team?

After determining readiness for microservices architecture, the next step is to structure the development team to design and implement them. In this tip, Kanjilal stresses that a proper structure can help enterprises meet business and development goals faster and with more efficiency. But what type of team is right for you? Three appropriate microservices team structures are:

Conway’s Law. This law is an observation that the design of any system is significantly affected by the communications structure of the development team. Build vertical teams in which each team can handle the UI, APIs, business logic and data access layers.

Build-and-run team. Have a team that is responsible for a single piece of software from end-to-end and can function independently from other teams.

Decentralized structure. Rather than separate teams into silos with no overlap, take a decentralized approach to reduce delays.

Establish the right team culture. Business and software team leaders should work closely with development teams. Development teams should be empowered and have the freedom to make technological recommendations, such as tooling, to the architect.

How do we migrate to microservices?

With your team ready, plan the migration to microservices. First, find the appropriate service granularity for the application source code and design. The wrong service size, too large or too small, can create complexities and overhead costs. Coach the development team to create services that are as independent and self-sufficient as possible.

Next, ensure developers have a diverse tech stack and a framework that can manage front-end components independently from the back-end infrastructure.

Finally, start the migration by decomposing the monolith. Rely on proven techniques, such as the strangler pattern that gradually replaces old systems with new ones, to minimize user-facing disruption.

According to Twain Taylor, analyst, monitoring is vital to a migration to microservices. Two areas that enterprises need to address are services communication and service failure.

Microservices execute independently but communicate to fulfill a user request. Designers might use an API gateway to manage external communication. For service-to-service communications, add a distributed tracing tool to gain more visibility. Additionally, consider a service mesh to enhance service discovery and monitoring capabilities.

Service failures shouldn’t take down an app. A popular option is the circuit breaker pattern in which a trip mechanism breaks the connection between services in the event of a fault. In this pattern, it retries the connection to test it and restores the connection when the failure is fixed.

5 big pitfalls to avoid during migration

Moving from a monolith to a microservices-based architecture is an admirable task, but development teams can find themselves in a heap of trouble in the process. Taylor warns that teams must absolutely avoid these five migration pitfalls:

1. Moving too quickly. Review the state of existing apps and verify that the organization has the required Agile and DevOps skills.

2. Making services too small. If teams break their legacy applications into services that are too granular, they will end up with more services than they can manage.

3. Coupling services too tightly. Development teams migrating from a monolithic approach to microservices tend to couple the services too tightly, which can create what is known as a distributed monolith.

4. Storing logs in containers. It is an intense process to log microservices, especially when logs are placed inside distributed container instances and frequently terminated. There are better options.

5. Failure to consider monitoring. The migration to microservices introduces a frightening monitoring scenario, as services are often developed on different stacks. Expect tooling investments.

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A team of researchers at Nanyang Technological University in Singapore claim deep reinforcement learning (DRL) algorithms — algorithms that have been used to predict the shapes of proteins and teach robots to grasp objects — are prone to adversarial attacks that can extract and replicate them, enabling malicious actors to “steal” them. In a preprint paper, the coauthors describe a technique that targets black-box models whose inputs and operations aren’t publicly exposed, and that purportedly recovers DRL models with “[very] high fidelity.”

DRL has gained currency in part thanks to its ability to handle complex tasks and environmental interactions. It incorporates deep learning architectures and reinforcement learning algorithms to build sophisticated policies, which can understand an environment’s context (states) and make optimal decisions (actions). But as DRL increasingly makes its way into commercialized products like Mobileye‘s and Wayve‘s advanced driver assistance systems, it risks becoming a target for adversaries intent on IP theft or potentially harmful reverse-engineering.

The researchers’ approach assumes some knowledge of a target DRL’s domain (i.e., the task the model is performing, the context, and the formats of the inputs and outputs) and that an attacker can set environmental states and observe the DRL model’s corresponding actions. Their attack occurs in two stages:

1. A classifier predicts the training algorithms of a given black-box DRL model based on its action sequence.
2. Informed by the extracted algorithms, an imitation learning method generates and fine-tunes a model with similar behaviors as the target.

The classifier is first trained on a large quantity of “shadow” DRL models underpinned by algorithms. Drawing on a diverse pool that includes all algorithms under consideration, the classifier trains DRL models for each algorithm across several environments and evaluates their performance. It then collects the state-action sequences of the best-performing models and generates samples (with the sequences as the feature and the training algorithm as the label) and passes the extracted model to the second stage for refinement.

The second stage — the imitation learning stage — employs GAIL, a model-free learning algorithm that imitates complex behaviors in large-scale and high-dimensional environments. Two models are constructed to contest with each other during the imitation process: a generative DRL model with the extracted algorithm and a discriminative model. The generative model iteratively refines its parameters based on feedback until it can’t distinguish the generated data from the target model, a process that repeats until it obtains a model with performance similar to that of the target.

In experiments, the researchers applied their approach to two popular benchmarks in OpenAI’s Gym software: Cart-Pole and Atari Pong. For each environment, they selected 50 trained models, which resulted in 250 trained DRL models that produced 12,500 sequences of actions.

They found that the classifier distinguished the DRL models of each algorithm with relatively high confidence, ranging from 54% (in Cart-Pole) to 100% (in Atari Pong). As for the imitation learning phase, it managed to replicate a model with the same algorithm that reached similar performance as the target model, particularly in Cart-Pole. “We observe that the [attack] success rate increases when the replicated model has the same training algorithm as the target model,” wrote the researchers. “We expect this study can inspire people’s awareness about the severity of DRL model privacy issues, and come up with better solutions to mitigate such model attacks.”

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Artificial Intelligence thrives on data. In recent times AI has been instrumental in creating new teaching and learning solutions that are now undergoing testing in different contexts. The beauty of any AI application is that it becomes more accurate when there is more accurate data available. Any AI application uses massive amount of data, and then the intelligence is built on it.

One very interesting aspect of the education sector is that the data that is available is accurate and also is built year on year. This happens both in the school ecosystem and also in the undergraduate and postgraduate education. Given that big data enables AI to reach its full potential, it would be fair to say that there is no data-driven AI without big data. This big data environment can be built at the country level if there is an infrastructure that is created by the government, which can give immense insights into the scenario of education in the country.

As on date, two areas have evolved in AI and ML with a special focus on education. They are learning analytics (LA) and educational data mining (EDM). Both of them overlap each other in terms of objectives and techniques. While EDM methods are drawn from diverse disciplines, including data mining, machine learning, psychometrics of statistics, information visualisation, and computational modeling, LA is more focused on learning content management systems and large-scale test results. LA combines institutional data, statistical analysis, and predictive modeling to identify which learners need help and how instructors can change academic behaviour.

While AI has great potential in the education space, India is a vast county, and the challenges that are thrown are unique and cannot be compared with other nations that have implemented AI and ML in education. I present the challenges that exist in applying and adopting AI and ML in the Indian Educational framework.

Lack of reliable high data: While the government has taken steps and initiative to collect data across higher education institutions, there is still a huge gap that exists between the ‘actual’ data and the data that is pulled by information systems.

Although the government launched National Academic Depository (NAD) with an objective to provide an online store that can hold all academic awards like the certificates, diplomas, degrees, mark-sheets, etc., duly digitised and lodged by academic institutions or boards or eligibility assessment bodies, it still has a long way to go and the work is still in progress. Unless there is a deadline that is set for all previous records to be updated, applying AI tools on this data will not be possible.

Lack of data at the district state and regional level: Many India educational still don’t have internal data that is available to the grassroots level. Many educational institutions still don’t have an ERP implemented, and hence, there is no central repository that collects data for further intelligence to be drawn from it. This is another challenge for implementing AI and ML, which can bring great insights and can help the teaching learning process.

The digital divide

India is a country that has great mobile penetration and also with new mobile players offering free data has made usage of internet reach tier -2 and tier -3 cities and towns across India. In-spite of mobile penetration at one end of the spectrum, there are schools that still lack basic facilities. There are engineering colleges that don’t even have digital projectors in the classroom and attendance being taken using the pen and paper method. One the other end of the spectrum there are schools which have unleashed the power of digital and automation solutions have been implemented to manage all the academic and administrative processes of the school or the higher educational institution to that extent that even Internet of things (IoT) applications are used for a specific process. This digital divide is a big challenge in implementing AI and ML.

While the applications of AI and learning analytics platforms can be used and the predictive algorithms can help teachers diagnose and anticipate learning difficulties faced by learners to implement personalised interventions to respond to those difficulties, are teachers equipped to use the same is a billion-dollar question? My personal experience has again made me see divergent scenarios. I have seen teachers who can’t even create and use a power point presentation in some schools and higher educational institutions, and I have also seen teachers who use learning analytics to find which student must be given focus based on the past and present academic records.

This is again a challenge to implement AI and ML across all educational institutions in India. Some policy changes have to created, and training must be made mandatory for teachers across all levels across the length and breadth of the country. Even if the government comes with the policy measure to train teachers, efforts have to be taken by teachers, and they must prepare, understand and grasp the new technological possibilities that digital and AI-powered tools can help education. MOOCs (Massive Open Online Courses) can be of great help in making this happen.

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