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Looking for a fun introduction to AI with a sense of humor? Look no further than Making Friends with machine learning (MFML), a lovable free YouTube course designed with everyone in mind. Yes,everyone. If youre reading this, the course is for you!

Image by Randall Munroe,xkcd.comCC.

Short form videos:Most of the videos below are 15 minutes long, which means you get to upgrade your knowledge in bite-sized, well, bites. Tasty bites! Dive right in at the beginning or scroll down to find the topic youd like to learn more about.

Long form videos:For those who prefer to learn in 12 hour feasts, the course is also available as 4 longer installmentshere.

Making Friends with machine learningwas an internal-only Google course specially created to inspire beginners and amuse experts.* Today, it is available to everyone!

The course is designed to give you the tools you need for effective participation in machine learning for solving business problems and for being a good citizen in an increasingly AI-fueled world. MFML is perfect for all humans; it focuses on conceptual understanding (rather than the mathematical and programming details) and guides you through the ideas that form the basis of successful approaches to machine learning. It has something for everyone!

After completing this course, you will:

I was simply blown away by the quality of her presentation. This was a 6-hour(!) tour de force; through every minute of it, Cassie was clear, funny, energetic, approachable, insightful and informative.Hal Ableson, Professor of Computer Science at MIT

I cannot emphasize enough how valuable it was that this course was targeted towards a general audience. Human resources specialist

Fantastic class, plus it is hilarious! Software engineer

I now feel more confident in my understanding of ML Loved it. Communications manager

More useful than any of the courses I took in university on this stuff. Reliability engineer

I loved how she structured the course, knowing the content, and navigating this full-day course without getting us bored. So I learned two things in this lesson. 1) Machine learning, and 2) Presentation skills. Executive

Great Stuff: I would recommend it. ML Research Scientist

always interesting and keeps my attention. Senior leader, Engineering

well structured, clear, pitched in at the right level for people like me and full of useful visuals and stories to help me understand and remember. I learnt a ton. Senior leader, Sales

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Everything Youve Ever Wanted to Know About Machine Learning - KDnuggets

In 2020, the U.S. insurance industry was worth a whopping $1.28 trillion. The American insurance industry is one of the largest markets in the world. The massive amount of premiums means there is an astronomical amount of data involved. Without artificial intelligence technology like machine learning, insurance companies will have a near-impossible time processing all that data, which will create greater opportunities for insurance fraud to happen.

Insurance data is vast and complex, composed of many individuals with many instances and many factors used in determining the claims. Moreover, the type of insurance increases the complexity of data ingestion and processing. Life insurance is different from automobile insurance, health insurance is different from property insurance, and so forth. While some of the processes are similar, the data can vary greatly.

As a result, insurance enterprises must prioritize digital initiatives to handle huge volumes of data and support vital business objectives. In the insurance industry, advanced technologies are critical for improving operational efficiency, providing excellent customer service, and, ultimately, increasing the bottom line.

ML can handle the size and complexity of insurance data. It can be implemented in multiple aspects of the insurance practice, and facilitates improvements in customer experiences, claims processing, risk management, and other general operational efficiencies. Most importantly, ML can mitigate the risk of insurance fraud, which plagues the entire industry. It is a big development in fraud detection and insurance organizations must add it to their fraud prevention toolkit.

In this post, we lay out how insurance companies are using ML to improve their insurance processes and flag insurance fraud before it affects their bottom lines. Read on to see how ML can fit within your insurance organization.

ML is a technology under the AI umbrella. ML is designed to analyze data so computers can make predictions and decisions based on the identification of patterns and historical data. All of this is without being explicitly programmed and with minimal human intervention. With more data production comes smarter ML solutions as they adapt autonomously and are constantly learning. Ultimately, AI/ML will handle menial tasks and free human agents to perform more complex requests and analyses.

There are several use cases for ML within an insurance organization regardless of insurance type. Below are some top areas for ML application in the insurance industry:

For insurers and salespeople, ML can identify leads using valuable insights from data. ML can even personalize recommendations according to the buyer's previous actions and history, which enables salespeople to have more effective conversations with buyers.

For a majority of customers, insurance can seem daunting, complex, and unclear. It's important for insurance companies to assist their customers at every stage of the process in order to increase customer acquisition and retention. ML via chatbots on messaging apps can be very helpful in guiding users through claims processing and answering basic frequently asked questions. These chatbots use neural networks, which can be developed to comprehend and answer most customer inquiries via chat, email, or even phone calls. Additionally, ML can take data and determine the risk of customers. This information can be used to recommend the best offer that has the highest likelihood of retaining a customer.

ML utilizes data and algorithms to instantly detect potentially abnormal or unexpected activity, making ML a crucial tool in loss prediction and risk management. This is vital for usage-based insurance devices, which determine auto insurance rates based on specific driving behaviors and patterns.

Unfortunately, fraud is rampant in the insurance industry. Property and casualty insurance alone loses about $30 billion to fraud every year, and fraud occurs in nearly 10% of all P&C losses. ML can mitigate this issue by identifying potential claim situations early in the process. Flagging early allows insurers to investigate and correctly identify a fraudulent claim.

Claims processing is notoriously arduous and time-consuming. ML technology is a tool to reduce processing costs and time, from the initial claim submission to reviewing coverages. Moreover, ML supports a great customer experience because it allows the insured to check the status of their claim without having to reach out to their broker/adjuster.

Fraud is one of the biggest problems for the insurance industry, so let's return to the fraud detection stage in the insurance lifecycle and detail the benefits of ML for this common issue. Considering the insurance industry consists of more than 7,000 companies that collect more than $1 trillion in premiums each year, there are huge opportunities and incentives for insurance fraud to occur.

Insurance fraud is an issue that has worsened since the COVID-19 pandemic began. Some industry professionals believe that the number of claims with some element of fraud has almost doubled since the pandemic.

Below are the various stages in which insurance fraud can occur during the insurance lifecycle:

Based on the amount of fraud and the different types of fraud, insurance companies should consider adding ML to their fraud detection toolkits. Without ML, insurance agents can be overwhelmed with the time-consuming process of investigating each case. The ML approaches and algorithms that facilitate fraud detection are the following:

ML is instrumental in fraud prevention and detection. It allows companies to identify claims suspected of fraud quickly and accurately, process data efficiently, and avoid wasting valuable human resources.

Implementing digital technologies, like ML, is vital for insurance businesses to handle their data and analytics. It allows insurance companies to increase operational efficiency and mitigate the top-of-mind risk of insurance fraud.

Read more:
5 use cases for machine learning in the insurance industry - Digital Insurance

Sep 12: Digitization has changed many sectors across the globe and that also include the logistic sector. With digitization, machine learning and artificial intelligence have become the norm. Logistic sectors have been implementing machine learning and artificial intelligence to innovate the sector and improve it further. The usage of artificial intelligence and machine learning has improved the productivity of the logistic sector. According to a report by Katrine Spina and Anastasiya Zharovskikh, the productivity of the logistic sector will increase by 40% by 2035 with the help of artificial intelligence and machine learning.

With the help of big data, logistic companies have been helpful in making clear predictions that were useful to improve their performance. Visibility and prediction have become possible due to the implementation of artificial intelligence and machine learning in the logistic sector. Here is how machine learning and artificial intelligence has been helpful in the logistic sector.

1. Robotics can be used to help the workforce

Including robotics in the logistic sector has been helpful in logistic companies likeDelhivery primarily with autonomous navigation. It has also further reduced the burden from the workforce and has been helpful in providing cost-effective solutions. Automated robots in the logistic sectors have been helpful in material selection and handling, long-haul distribution along last-mile delivery.

2. Warehouse management and optimization of supply chain planning

Warehouse management in the logistic sector can only be optimized when it is accurately predicted when things need to be moved and what equipment is needed to handle it. This can improve the overall productivity of the warehouse. Accuracy of such predictions is possible with the help of big data. Also, with the help of contextual intelligence, effective planning can be made in logistic companies like Ekart. AI-based solutions are helpful in forecasting demand and machine learning can also be applied in order to improve the efficiency of the supply chain too.

3. Autonomous vehicles

Autonomous vehicles have become popular all across the world and it would not have been possible if artificial intelligence did not exist. Artificial intelligence allows autonomous vehicles to perceive and then further, predict the changes in the environment with the help of sensing technologies. With autonomous vehicles, last-mile delivery can be fastened. Many logistic companies have been experimenting with autonomous vehicles as a part of their development strategy and Google and Tesla have been working hard towards this sector.

4. Improved customer experience

Gone are the days when the general queries of the customers used to be handled by real people. Thankfully, customer experiences are now handled with the help of chatbots and this has made things so much easier in ensuring a satisfactory customer experience. Many companies have accepted that the customer experience played a vital role in the growth of the company. The use of artificial intelligence in customer experience has been helpful in improving customer loyalty and retention with personalization.

5. Efficient planning and resource management

For the growth of any business and not just the logistic sector, efficient planning and resource management are important. Artificial intelligence plays a key role in efficient planning and resource management by helping companies to reduce the cost and optimize the movement of commodities, which also improves the supply chain of the logistic sector in real-time.

6. Time Route Optimization

Artificial intelligence also makes it possible for real-time route optimization which increases the efficiency of the delivery and thereby, helps in reducing the waste of resources. Many logistics companies have already been using an autonomous delivery system which has made it possible to deliver items at a much quicker pace and that too without the requirement of human labor. Artificial intelligence has always been helpful in freight management by helping in efficient logistic management by lowering the shipping costs and improving the delivery process.

In addition to the factors mentioned above, machine learning and artificial intelligence also help in demand prediction, sales and marketing optimization, product inspection and back-office automation. Competitive advantage will be in the hands of logistic sectors that use artificial intelligence and machine learning for the growth of the company. The current demands of the customers include real-time visibility, super-fast deliveries and it is possible to meet such expectations of the customers only by accepting technology in the logistics sector.

Read the original:
How Machine Learning And AI Is Transforming The Logistic Sector? - Daijiworld.com

Workday, which specializes in cloud-based accounting and human resources software, announced new machine learning and automation features in several of its products.

Workday Adaptive Planning will now sport a Machine Learning Forecaster that allows automated generation of forecasts that can incorporate historical or third-party data like weather reports and labor statistics. Workday said the software enhancements have led to a more than 60% speed improvement for data import and export.

Workday Strategic Sourcing, meanwhile, now has a contract automation feature that extracts key metadata and clauses from third-party paper and legacy contracts to aid in identifying and searching for key contract terms, as well as uncovering risks and managing contract obligations.

Workday Expenses will have a new Expense Protect feature that will automatically detect potential duplicate expenses, which will reduce the need for manual review.

The company also announced new solutions for environmental, social and governance-related reporting. Workday Strategic Sourcing now has supplier diversity discovery boards that can provide data about supplier diversity ratios. Further, a new solution called Workday Supplier Sustainability gives users information about their suppliers' science-based targets, actual and derived CO2 emissions, and their ESG ratings from third-party analysts.

The company also announced an Industry Accelerators program to help organizations transition operations to Workday. The Industry Accelerators combine industry practices, solutions and connectors for banking, health care, insurance and technology companies. They will also help automate and streamline operations for customers.

"While it's a complex environment for finance professionals, it's also an opportunity for them to partner more closely with the business to mitigate risk and surface valuable insights for their organization," said Terrance Wampler, group general manager of the office of the CFO at Workday, in a statement. "At Workday, our innovations are aimed at helping advance the finance function by streamlining business processes in the cloud and accelerating data analysis so teams can respond faster and take action."

More here:
Workday announces new machine learning and automation capacities on product line - Accounting Today

The health of the planet is one of the most important challenges facing humankind today. From climate change to unsafe levels of air and water pollution to coastal and agricultural land erosion, a number of serious challenges threaten human and ecosystem health.

Ensuring the health and safety of our planet necessitates approaches that connect scientific, engineering, social, economic, and political aspects. New computational methods can play a critical role by providing data-driven models and solutions for cleaner air, usable water, resilient food, efficient transportation systems, better-preserved biodiversity, and sustainable sources of energy.

The MIT Schwarzman College of Computing is committed to hiring multiple new faculty in computing for climate and the environment, as part of MITs plan to recruit 20 climate-focused faculty under its climate action plan. This year the college undertook searches with several departments in the schools of Engineering and Science for shared faculty in computing for health of the planet, one of the six strategic areas of inquiry identified in an MIT-wide planning process to help focus shared hiring efforts. The college also undertook searches for core computing faculty in the Department of Electrical Engineering and Computer Science (EECS).

The searches are part of an ongoing effort by the MIT Schwarzman College of Computing to hire 50 new faculty 25 shared with other academic departments and 25 in computer science and artificial intelligence and decision-making. The goal is to build capacity at MIT to help more deeply infuse computing and other disciplines in departments.

Four interdisciplinary scholars were hired in these searches. They will join the MIT faculty in the coming year to engage in research and teaching that will advance physical understanding of low-carbon energy solutions, Earth-climate modeling, biodiversity monitoring and conservation, and agricultural management through high-performance computing, transformational numerical methods, and machine-learning techniques.

By coordinating hiring efforts with multiple departments and schools, we were able to attract a cohort of exceptional scholars in this area to MIT. Each of them is developing and using advanced computational methods and tools to help find solutions for a range of climate and environmental issues, says Daniel Huttenlocher, dean of the MIT Schwarzman College of Computing and the Henry Warren Ellis Professor of Electrical Engineering and Computer Science. They will also help strengthen cross-departmental ties in computing across an important, critical area for MIT and the world.

These strategic hires in the area of computing for climate and the environment are an incredible opportunity for the college to deepen its academic offerings and create new opportunity for collaboration across MIT, says Anantha P. Chandrakasan, dean of the MIT School of Engineering and the Vannevar Bush Professor of Electrical Engineering and Computer Science. The college plays a pivotal role in MITs overarching effort to hire climate-focused faculty introducing the critical role of computing to address the health of the planet through innovative research and curriculum.

The four new faculty members are:

SaraBeerywill join MIT as an assistant professor in the Faculty of Artificial Intelligence and Decision-Making in EECS in September 2023.Beeryreceived her PhD in computing and mathematical sciences at Caltech in 2022, where she was advised by Pietro Perona. Her research focuses on building computer vision methods that enable global-scale environmental and biodiversity monitoring across data modalities, tackling real-world challenges including strong spatiotemporal correlations, imperfect data quality, fine-grained categories, and long-tailed distributions. She partners with nongovernmental organizations and government agencies to deploy her methods in the wild worldwide andworks towardincreasing the diversity and accessibility of academic research in artificial intelligence through interdisciplinary capacity building and education.

PriyaDontiwill join MIT as an assistant professor in the faculties of Electrical Engineering and Artificial Intelligence and Decision-Making in EECS in academic year 2023-24.Donti recently finished her PhD in the Computer Science Department and the Department of Engineering and Public Policy at Carnegie Mellon University, co-advised by Zico Kolter and Ins Azevedo. Her work focuses on machine learning for forecasting, optimization, and control in high-renewables power grids. Specifically, her research explores methods to incorporate the physics and hard constraints associated with electric power systems into deep learning models. Donti is alsoco-founder and chair of Climate Change AI, a nonprofit initiative to catalyze impactful work at the intersection of climate change and machine learning that is currently running through the Cornell Tech Runway Startup Postdoc Program.

Ericmoore Jossou will join MIT as an assistant professor in a shared position between the Department of Nuclear Science and Engineering and the faculty of electrical engineering in EECS in July 2023. He is currently an assistant scientist at the Brookhaven National Laboratory, a U.S. Department of Energy-affiliated lab that conducts research in nuclear and high energy physics, energy science and technology, environmental and bioscience, nanoscience, and national security. His research at MIT will focus on understanding the processing-structure-properties correlation of materials for nuclear energy applications through advanced experiments, multiscale simulations, and data science. Jossou obtained his PhD in mechanical engineering in 2019 from the University of Saskatchewan.

SherrieWangwill join MIT as an assistant professor in a shared position between the Department of Mechanical Engineering and the Institute for Data, Systems, and Society in academic year 2023-24. Wangis currently a Ciriacy-Wantrup Postdoctoral Fellow at the University of California at Berkeley, hosted by Solomon Hsiang and the Global Policy Lab. She develops machine learning for Earth observation data. Her primary application areas are improving agricultural management and forecasting climate phenomena. She obtained her PhD in computational and mathematical engineering from Stanford University in 2021, where she was advised by David Lobell.

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Computing for the health of the planet - MIT News