Mediaboss Marketing

ML and AI are essential drivers of innovation and growth in all sectors, including education.

Machine Learning (ML) and Artificial Intelligence (AI) are essential drivers of innovation and growth in all sectors, including education.

While AI-powered technologies have been around for a while in EdTech, the sector has been sluggish in their acceptance. The pandemic, on the other hand, radically altered the scene, pushing educators to rely on tech for virtual instruction. Now, 86 percent of educators believe that technology should be an integral element of education. AI has the potential to improve both learning and teaching, assisting the education industry, simultaneously evolving to benefit both students and teachers.

Here is how AI can benefit both the students and the educators:

To be precise, a students sole purpose of going to an educational institute is to get a degree or credential demonstrating their expertise. AI can have a huge impact on students educational journeys by offering access to the relevant courses, enhancing contact with teachers, and allocating more time to work on other aspects of life. Here are a few examples:

Personalization is one of the most prominent educational trends. Students now have a customized way of learning programs that focus on their own distinct experiences and interests; thanks to AI applications. AI can adjust to each students level of expertise, learning speed, and desired goals to ensure they get the most from their learning. Furthermore, AI-powered systems can examine students previous educational histories, detect shortcomings, and recommend courses better suited for improvement, allowing for a highly personalized learning opportunity.

While it is not unusual for kids to require additional assistance outside of the class, many educators would not have the time to assist children after school. While no chatbot can really replace a teacher, AI programs can assist students in honing their skills outside the classroom by helping with improving on weak areas. They offer one-on-one experiential learning without the teacher being available to answer questions at all hours of the day. In addition, an AI-powered bot can respond to queries in 2.7 seconds.

Nothing is more aggravating than posing a question and having it answered 2 days later. On a regular basis, teachers and instructors are assaulted with repetitious queries. With the support of automation and cognitive intelligence, AI can assist students to get solutions to their most frequently asked questions in seconds. This not only saves teachers a lot of time but also students time looking for answers or awaiting a response to their inquiries.

AI-powered solutions make learning available to all students, at any time and from any location. Each learner has his own pace, and having 24/7 access allows kids to experiment with what works best for them without having to wait for an educator. Furthermore, students from all around the world can obtain high-quality learning without paying travel or living fees.

Most teachers and staff arent ashamed to say they battle with time management, which makes sense given the number of tasks on their daily to-do lists. By automating chores, assessing student performance, and eliminating the educational gap, AI can assist in freeing up teachers time. Heres how it works:

Like AI can customize learning education courses for students, it can also assist teachers in their work. AI can provide teachers with a clear image of subjects and courses which need revaluation, by studying students learning capacities and histories. This study enables teachers to design the most effective learning plan for every single student. By studying each students particular needs, teachers and lecturers can tailor their courses to meet the most prevalent knowledge gaps or issue areas before a learner falls far behind.

AI-powered chatbots with accessibility to a schools entire base of knowledge can answer a range of generic and repetitive inquiries students commonly have without having to contact a faculty member. This way, AI frees up time for teachers to concentrate on curriculum design, coursework research, and ways of increasing student engagement.

AIs potential to automate the most basic job includes tasks such as replacing administrative labour, grading papers, measuring learning patterns, responding to general questions, etc. A Telegraph poll found that teachers spend 31% of their time organizing courses, grading tests, and doing administrative duties. Teachers, on the other hand, can use support automation systems to automate manual tasks, giving themselves more time to concentrate on improving their teaching competency.

Share This ArticleDo the sharing thingy

See the article here:
What is the Role of Artificial Intelligence in the Education Sector? - Analytics Insight

After a long winter, the artificial intelligence field has seen a resurgence in the past 15 years as computer power increased and a lot of digital data became available. In the past few years alone, giant language models advanced so quickly to outpace benchmarks, computer vision capabilities took self-driving cars from the lab to the street, and generative models tested democracies during major elections.

But parallel to this technologys rapid rise is its potential for massive harm; technologists, activists, and academics alike began calling for better regulation and understanding of its impact.

This spring, Stanford Institute for Human-Centered AI (HAI) will address three of the most critical areas of artificial intelligence during a one-day conference free and open to all:

Stanford HAI Associate Director and linguistics and computer science professor Christopher Manning, who will cohost the event with HAI Denning Co-director and computer science professor Fei-Fei Li, explains what this conference will cover and who should attend.

This conference will look at key advances in AI. Why are we focusing on foundation models, accountable AI, and embodied AI? What makes these the areas where you expect major growth?

An enormous amount of work is going on in AI in many directions. For a one-day event, we wanted to focus in on a small number of areas that we felt were key to where the most important and exciting research might appear this decade. We ended up focusing on three areas. First, there has been enormous excitement and investment around the development of large pre-trained language models and their generalization to including multiple data modalities that we have named foundation models. Second, there has been an exciting resurgence of work linking AI and robotics, often enabled by the use of simulated worlds, which allow the exploration of embodied AI and grounding. Finally, the increasing concerns about understanding AI decisions and maintaining data privacy in part demand societal and regulatory solutions, but they are also an opportunity for technical AI advances as to how you can produce interpretable AI systems or systems that still work effectively on data that is obscured for privacy reasons.

Who are you excited to hear from?

Ilya Sutskever has been one of the central people at the heart of the resurgence of deep learning-based AI, starting from his breakthrough work on the computer vision system AlexNet with Geoff Hinton in 2012. His impact has grown since he became the chief scientist of Open AI, which among other things has led in the development of foundation models. Im looking forward to hearing more about their latest models such as InstructGPT and what he sees lying ahead.

The recent successes in AI just would not have been possible without the amazing breakthroughs in parallel computing largely led by NVIDIA. Bill Dally is a leader in computer architecture, and, for the last decade, he has been the chief scientist at NVIDIA. He can give us powerful insights into the recent and future advances in parallel computing via GPUs but also insights into the broader range of vision, virtual reality, and other AI research going on at NVIDIA.

And Hima Lakkaraju is a trailblazing Harvard professor developing new strands of work in trustworthy and interpretable machine learning. When AI models are used in high-stakes settings, most times people would like accurate and reliable explanations of why the systems make certain decisions. One exciting direction in Himas work is in developing formal Bayesian models that can give reliable explanations.

Who should attend this conference?

Through a combination of short talks and panel discussions, were trying to achieve a balance between technical depth and accessibility. So on the one hand this conference should be of interest to anyone working in AI as a student, researcher, or developer, but beyond that we hope to be able to convey some of the excitement, results, and progress in these areas to anybody with an interest in AI, whether as a scientist, decision maker, or concerned citizen.

What do you hope your audience will take away from this experience?

I hope the audience will get a deeper understanding of how AI has been able to advance so quickly in the last 15 years, where it might go next, and what we should and shouldnt worry about. I hope people will take away the awesome powers of the huge new foundation models that are being built. But equally they will see why building a model from mountains of digital data is not sufficient, and we want to explore embodied AI models in a physical or simulated world that can learn more as babies learn. And finally, we will see something about how there is now a lot of exciting technical work underway to address the worries and downsides of AI that have been very prominently covered in the media in recent years.

Interested in attending the 2022 HAI Spring Conference? Learn more or register.

Link:
What's Next in Artificial Intelligence? Three Key Directions - Stanford HAI

Ilias Tagkopoulos,University of California, Davis

Nitin Nitin, University of California, Davis

The worlds food system is ripe for disruption in an unprecedented way. There are many challenges that we are facing both domestically and worldwide. According to the EPA, about a third of the food supply is lost, accounting for 15% of municipal solid waste and 2% of our energy use. Pesticide and herbicide use has increased more than 10% over the past 5 years while not everyone has access to high-quality food they can affordwhich is partially responsible for 40% of U.S. adults and 18% of our teenagers being obese, while one in eight families in America are hungry.

Many of these challenges are caused by inefficiencies in the food processing and distribution supply chain, which is a vital value-added step in our food system. The pieces of the puzzle are all there: ubiquitous sensors and devices that generate data with unprecedented volume, velocity and veracity; mature computational methods to make use of them; connected markets that can take advantage of these innovations at a global scale; and a need to transform antiquated, obsolete components of the current system, whether because of consumer demand for personalization and empowerment, or the need for global food safety and sustainability. Millions are spent every year in both the private and public sector to bring forth innovative solutions in capturing market preference, food safety, food security, provenance and traceability, all the while creating superior products that taste good, are good for your health and dont break the bank.

Recently, a strategic initiative by the National Science Foundation (NSF) and the U.S. Department of Agriculture (USDA) has led to the birth of the USDA/NSF AI Institute for Next Generation Food Systems (AIFS). AIFS is a $20 million collaboration between UC Davis, UC Berkeley, UC Agricultural and Natural Resources, University of Illinois and Cornell University, with the mission to develop and leverage transformative AI for the ethical production and distribution of safe, sustainable, nutritious food with fewer resources. With more than 50 faculty participating, AIFS aspires to use AI as the connective tissue that brings together the different segments of the food system, from molecular breeding and agricultural production to food processing and consumer nutrition.

Food safety risks are the leading causes of food recalls in the industry and a significant impact on health, social and economic aspects of our society. These risks range across diverse categories of food products, including dairy, meat, fresh produce and raw dry powders, while their main cause is contamination of food products with microbial pathogens in the harvesting and processing environments. The sources of these contaminations are diverse and often are not detected a priori with conventional testing. That is due to a lack of comprehensive sampling techniques and services that can provide accurate results in an inexpensive and timely manner.

This is where the trifecta of next-generation sequencing, artificial intelligence and cyber-physical systems can have a multiplier effect in keeping our food supply safe. Traditional 16s gene sequencing and, more recently, metagenomics sequencing, together with rapid identification of the microbial consortia in a sample, can quickly detect the presence of dangerous pathogens, such as Listeria monocytogenes.

AI-driven algorithms can be trained to assess the outbreak risk level by calculating the relative abundances of the various microbes in factories and food processing facilities. This can eventually lead to an always-on alert and recommendation system that can predict potential contamination and recommend corrective actions to reduce the risk of outbreaks and the cost of product recalls.

Furthermore, digital twins can serve as a clone digital replica of the factories, distribution centers and other areas where contamination is possible, providing a platform to evaluate hypotheses, optimize solutions and better understand system dynamics while constantly integrating feedback from the physical system.

Eventually, AI-driven systems that have the flexibility to integrate the existing food microbial ecology, chemometric and physical data sets for a comprehensive assessment of food safety risks can revolutionize food safety and create a safer food system, from farm to fork.

The AI Institute for Food Systems Value Chain

AI enrichment through the food supply chain.

Many food processing operations such as sterilization of food products, drying and baking require significant energy and water resources. In addition, sanitation operations required for the hygiene of the processing equipment use a significant amount of energy, water and chemical resources. With emerging climate challenges, there is a marked need to develop solutions to address these challenges.

Many of these efforts have focused on conventional engineering approaches such as waste heat recovery and reuse of spent water resources. Digital twins can again help by becoming an inexpensive testbed, as they can provide a digital replica of a processing operation and enable real-time analysis of water, energy and chemical usage in a facility. With cloud computing and scientific computation techniques, practitioners can run millions of simulations in minutes to identify the parameters that lead to the best possible resource use. Adaptive techniques such as active learning can be used to incorporate feedback from the physical system and improve the systems performance in maximizing efficiency.

In addition to opportunities for process optimization, these combinations of AI and digital twin technologies can aid in process validation and verification. Process validation and verification are required by regulatory organizations to ensure the safety of food products. Validation and verification processes in the food industry often require inoculation of food with a surrogate microbe to target pathogens and its testing following processing. These are resource- and time-intensive processes. AI-enabled digital twin technologies and data analytics can provide real-time validation and verification of processing operations.

In addition, significant early-stage efforts have been made to AI solutions for quality evaluation of input and output streams from food processing operations. The development of AI-guided sorting of fresh produce, such as blueberries, has shown significant improvement in efficiency and reducing labor-intensive practices in the industry. There are opportunities to advance vision and sensor-guided sorting of input and output streams of diversity of food products to improve the quality of the products and reduce food waste. Despite these early successes, there are many other areas in the quality control of food processing operations that are still managed based on empirical human decision-making processes: e.g. consistency of pastes and juices derived from agricultural commodities, such as apples and tomatoes, which are largely managed based on human judgment.

AI can revolutionize food safety and create a safer food system.

Producing and making available food that is good for our health, wallet and taste palate is the Holy Grail of any food and nutrition company. AI can play a significant role here, both unlocking the mysteries of chemical composition of food and creating new functional products. A significant blocker for any AI chef is the lack of the molecular atlas of food, knowing at high resolution what is in each ingredient beyond the protein, fat and carbohydrate content that we have been used to for generations. Not all proteins or carbs have been born equal when it comes to what they do to our body and mind. Even when we know the compoundsmay they be small molecules, peptides, glycans or anything elsethat confer benefits and help us transition to a healthier state, each of us has different genetics and gut microbiota, which in turn lead to strikingly different responses.

The complexity of the food-host interaction is both fascinating and daunting, and this is exactly the space where visionary initiatives can have a transformative impact in our way of life. AI-driven product formulations that are tailored for the individual needs of target groups, and that are specific enough based on co-morbidities, age or biomarkers, can be a paradigm shift that will epitomize the Hippocratic Let food be thy medicine. Optimal food processing is an equally important and complementary task, as the texture, nutritional value and function of the final product depends on it, with many of those beneficial compounds being lost in the process.

In the next 5 years, we will witness a paradigm shift in how we perceive, produce and consume food. There are already significant efforts to adopt and adapt the latest in sensor technology and AI in various aspects of the food system, while a number of initiatives with significant funding have been focused on mapping the molecular composition of various food ingredients. The AI community has made major advances in creating explainable and interpretable AI solutions, with a focus on fairness, trustworthiness and the ability to predict even in areas where there is a scarcity of data. Taken together, we expect an adaptive radiation of solutions and a rich ecosystem of partnerships fueled by unprecedented innovation and a strong desire to bring forth the next generation of food systems for a better tomorrow.

Dr. Ilias Tagkopoulos is a professor of computer science and the Genome Center at the University of California, Davis, where he leads the Integrative Biology and Predictive Analytics laboratory. He also is the director of the USDA-NIFA/NSF AI Institute of Next-Generation Food Systems (AIFS), a seven-institute collaboration. His work addresses data integration, modeling, design and decision-making under uncertainly, with applications in clinical and nutrition data. He holds a MSc from Columbia and a Ph.D. from Princeton in electrical engineering.

Dr. Nitin Nitin is co-principal investigator and lead of the Food Processing and Distribution Cluster at the Artificial Intelligence Institute for Next Generation Food Systems (AIFS). He is a professor in the departments of food science and technology and biological and agricultural engineering at the University of California, Davis. His research focuses on improving food quality and safety by developing innovative solutions focused on food processing, encapsulation, novel antimicrobials, biosensors and imaging. He holds Ph.D.s in bioengineering and food engineering.

Read the original here:
Inside AI: Food Processing and Distribution in the Era of Artificial Intelligence - Inside Unmanned Systems

The apps machine-learning algorithm can identify patterns and learn from data with human help. It analyzes data from different individuals blood sugar responses to tens of thousands of different meals to identify personal characteristics age, gender, weight, microbiome profile and various metabolic measurements that explain why one persons glucose spikes with certain foods when another persons doesnt. The algorithm uses these observations to predict how a particular food will affect ones blood sugar and assign each meal a score.

The system cant yet take into account the candy bar someone had two hours ago but users can play around with food combinations to change the score for each meal. For example, the app gave macaroni and cheese one of Mr. Idemas favorites a low score, but he was able to improve it by adding protein. Thats because adding protein or healthy fats can temper the blood sugar spike from a carbohydrate-heavy meal like macaroni.

I thought they were going to say, Oh my gosh, youve just got to become a salad eater, and thats not been the case, said Mr. Idema.

DayTwo, which is currently only available to employers or health plans, not consumers, is one of a handful of A.I.-based apps recommending healthier meal options. Another company, ZOE, also generates meal scores and is available directly to consumers for $59 per month. ZOEs algorithm uses additional data, such as blood fat levels, in addition to microbiome and blood sugar tests. The algorithm was able to predict how a persons blood sugar and fats respond to different foods in a large 2020 study led by one of the companys founders, Dr. Tim Spector, a professor of genetic epidemiology at Kings College in London.

Currently these algorithms mostly focus on blood sugar, but newer versions will incorporate more personal data, and, in theory, recommend diets that reduce cholesterol, blood pressure, resting heart rate or any other measurable clinical indicator.

Bringing in all these different data types is very, very powerful, and thats where machine learning kicks in, said Dr. Michael Snyder, a genetics professor at Stanford University who helped found the health start-up, January.

The field of personalized nutrition is still in its Wild West phase, and experts say its important to sort through the hype. Many companies are willing to test your microbiome and offer A.I.-driven dietary recommendations as well as sell you supplements but few are based on scientifically rigorous trials. Last year, uBiome, which made one, was even charged with fraud. In general, the more broad-ranging the health and weight loss claims the companies make, the less reliable the evidence to support them.

Visit link:
Here Come the Artificial Intelligence Nutritionists - The New York Times

NEEDHAM, Mass.--(BUSINESS WIRE)--Spending on artificial intelligence in the United States will grow to $120 billion by 2025, representing a compound annual growth rate (CAGR) of 26.0% over the 2021-2025 forecast period. Moreover, all 19 U.S. industries profiled in the latest Worldwide Artificial Intelligence Spending Guide from International Data Corporation (IDC) are forecast to deliver AI spending growth of 20% or more. The U.S. also accounts for more than half of all AI spending worldwide.

Retail will remain the largest U.S. industry for AI spending throughout the forecast while Banking will be the second largest industry. Together, these two industries will represent nearly 28% of all AI spending in the United States in 2025 and will account for nearly $20 billion of the amount added to the U.S. total over the forecast. The U.S. industries that will see the fastest growth in AI spending will be Professional Services, Media, and Securities and Investment Services, all of which will have CAGRs greater than 30%.

Within Retail, the AI use cases that will receive the most investment will be Augmented Customer Service Agents, and Expert Shopping Advisors & Product Recommendations. These two use cases encourage and assist increased spending by retail customers and account for nearly 40 percent of AI spending in the industry. The shift to online shopping contributes considerably to the adoption of AI within retail. AI spending in the Banking industry will be spread across several different functional areas, including customer service (Program Advisors and Recommendation Systems), operations (Fraud Analysis and Investigation), and security (Augmented Threat Intelligence and Prevention Systems).

Among the 30 AI use cases included in the Spending Guide, two will remain the largest in terms of total spending throughout the forecast Augmented Customer Service Agents and Sales Process Recommendation and Augmentation. Together, these two use cases will account for more than 20% of all AI spending in the U.S. in 2025. In terms of growth, two AI use cases (Public Safety and Emergency Response and Augmented Claims Processing) will have five-year CAGRs greater than 30% while a third use case (IT Optimization) will ride a CAGR of 29.7% to become the third largest AI use case in 2025.

"The greatest potential benefit for the use of AI remains its use in developing new business, and building new business models," said Mike Glennon, senior research manager with IDC's Customer Insights & Analysis team. "However, existing businesses are hesitant to embrace this potential, leaving the greatest opportunities to new market entrants that have no fear of change and can adapt easily to new ways of conducting business. The future for business is AI and those companies that can seize this opportunity could easily become the new giants."

The Worldwide Artificial Intelligence Spending Guide sizes spending for technologies that analyze, organize, access, and provide advisory services based on a range of unstructured information. The Spending Guide quantifies the AI opportunity by providing data for 30 use cases across 19 industries in nine regions and 32 countries. Data is also available for the related hardware, software, and services categories.

Taxonomy Note: The IDC Worldwide Artificial Intelligence Spending Guide uses a precise definition of what constitutes an AI Application in which the application must have an AI component that is crucial to the application without this AI component the application will not function. This distinction enables the Spending Guide to focus on those software applications that are strongly AI Centric. In comparison, the IDC Worldwide Semiannual Artificial Intelligence Tracker uses a broad definition of AI Applications that includes applications where the AI component is non-centric, or not fundamental, to the application. This enables the inclusion of vendors that have incorporated AI capabilities into their software, but the applications are not exclusively used for AI functions only. In other words, the application will function without the inclusion of the AI component.

About IDC Spending GuidesIDC's Spending Guides provide a granular view of key technology markets from a regional, vertical industry, use case, buyer, and technology perspective. The spending guides are delivered via pivot table format or custom query tool, allowing the user to easily extract meaningful information about each market by viewing data trends and relationships.

Click here to learn about IDC's full suite of data products and how you can leverage them to grow your business.

About IDCInternational Data Corporation (IDC) is the premier global provider of market intelligence, advisory services, and events for the information technology, telecommunications, and consumer technology markets. With more than 1,200 analysts worldwide, IDC offers global, regional, and local expertise on technology, IT benchmarking and sourcing, and industry opportunities and trends in over 110 countries. IDC's analysis and insight helps IT professionals, business executives, and the investment community to make fact-based technology decisions and to achieve their key business objectives. Founded in 1964, IDC is a wholly owned subsidiary of International Data Group (IDG), the world's leading tech media, data, and marketing services company. To learn more about IDC, please visit http://www.idc.com. Follow IDC on Twitter at @IDC and LinkedIn. Subscribe to the IDC Blog for industry news and insights.

Read more:
Spending on Artificial Intelligence Solutions Will Double in the United States by 2025, According to a New IDC Spending Guide - Business Wire