If youve heard the term machine learning, but arent quite sure what it means, then youve come to the right place. Machine learning is a type of artificial intelligence that allows software applications to become more accurate at predicting outcomes without being specifically programmed to do so. Basically, machine learning (MI) and artificial intelligence (AI) are helping businesses by improving customer service, reducing errors, managing automation and much more. Why do you need to know all of this? Well, for all of you out there looking to boost your income and career opportunities, you should consider this handy bundle that will give you the basics in machine learning.

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Be On The Cutting-Edge Of Tech With This Top-Rated Learning Bundle - IFLScience

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.

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How Machine Learning And AI Is Transforming The Logistic Sector? - Daijiworld.com

Apple machine learning projects span almost every aspect of the companys activities, but in a new speech at an AI conference, a senior exec spoke specifically about the benefits for accessibility and health.

Ge Yue, Apple VP and managing director of Apple Greater China, gave her speech at the 2022 World Artificial Intelligence Conference in Shanghai

NPR reports:

Apple has given a rare speech at a global AI gathering, with vice president Ge Yue choosing to concentrate on Machine Learning in accessibility features []

The company has chosen to illustrate the technology through accessibility features in Apple Watch, and AirPods Pro []

She said that Machine Learning plays a crucial role in Apples hope that its products can help people innovate and create, and provide the support they need in their daily lives.

We believe that the best products in the world should meet everyones needs, she continued. Accessibility is one of our core values and an important part of all products. We are committed to manufacturing products that are truly suitable for everyone.

We know that machine learning can help disabled users provide independence and convenience, she said, including people with the visually impaired, the hearing impaired, people with physical and motor disabilities, and people with cognitive impairment.

Ge Yue gave the example of the Assistive Touch feature on Apple Watch, which the company introduced last year, alongside eye-tracking on iPad.

To support users with limited mobility, Apple is introducing a revolutionary new accessibility feature for Apple Watch. AssistiveTouch for watchOS allows users with upper body limb differences to enjoy the benefits of Apple Watch without ever having to touch the display or controls.

Using built-in motion sensors like the gyroscope and accelerometer, along with the optical heart rate sensor and on-device machine learning, Apple Watch can detect subtle differences in muscle movement and tendon activity, which lets users navigate a cursor on the display through a series of hand gestures, like a pinch or a clench. AssistiveTouch on Apple Watch enables customers who have limb differences to more easily answer incoming calls, control an onscreen motion pointer, and access Notification Center, Control Center, and more.

She said that this utilized on-device machine learning.

This function combines machine learning on the device with data from the built-in sensors of Apple Watch to help detect subtle differences in muscle movement and tendon activity, thus replacing the display tapping.

Apple views accessibility as one of the companys core values, and its tech can make a huge difference to the lives of people with disabilities. One reader spoke earlier this year about small things making a big difference.

I always thought it bonkers when using Siri on iPhones, for years users can place a call by saying Hey Siri, call, but until now theres been no Hey Siri, end call command. It lead to a lot of daily frustration as I cant press the red button on the iPhone screen to hang up a phone call, so this prompted me to campaign for it. Im really glad Apple has listened and resolved the contradiction in iOS 16! Hopefully, it will also be of use to anyone who has their hands full.

That point is one others have echoed: Accessibility features may be aimed primarily at those with disabilities, but can often prove beneficial to a much wider audience.

Apple also sees machine learning having huge potential for future health features, says Ge Yue.

Saying, too, that our exploration in the field of health has just begun, she says that Apple believes that machine learning and sensor technology have unlimited potential in providing health insights and encouraging healthy lifestyles.

Photo: Xu Haiwei/Unsplash

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Originally posted here:
Apple machine learning speech focuses on benefits for accessibility and health - 9to5Mac

What youll learn:

Over the last two decades, compute-intensive artificial-intelligence (AI) tasks have promoted the use of custom hardware to efficiently drive these robust new systems. Machine-learning (ML) models, one of the most used forms of AI, are trained to handle those intensive tasks using floating-point arithmetic.

However, because floating-point formats have been extremely resource-intensive, AI deployment systems often rely on one of a handful of now-standard integer quantization techniques using floating-point formats, such as Google's bfloat16 and IEEE's FP16.

Since computer memory is limited, it's not efficient to store numbers with infinite precision, whether theyre binary fractions or decimal ones. This is due to the inaccuracy of the numbers when it comes to certain applications, such as training AI.

While software engineers can design machine-learning algorithms, they often can't rely on the ever-changing hardware to be able to efficiently execute those algorithms. The same can be said for hardware manufacturers, who often produce next-gen CPUs without being task-oriented, meaning the CPU is designed to be a well-rounded platform to process most tasks instead of target-specific applications.

When it comes to computing, floating-points are formulaic arithmetic representative of real numbers that are an approximation to support a tradeoff between range and precision, or rather tremendous amounts of data and accurate outcomes. Because of this, floating-point computation is often used in systems with minimal and large numbers that require fast processing times.

It's widely known that deep neural networks can tolerate lower numerical precision because high-precision calculations are less efficient when training or inferencing neural networks. Additional precision offers no benefit while being slower and less memory-efficient.

In fact, some models can even reach higher accuracy with lower precision. A paper released by Cornell University attributes to the regularization effects of the lower precision.

While there are a ton of floating-point formats, only a few have gained traction for machine-learning applications as those formats require the appropriate hardware and firmware support to run efficiently. In this section, we will look at several examples of floating-point formats designed to handle machine-learning development.

IEEE 754

The IEEE standard 754 (Fig. 1) is one of the widely known formats for AI apps. Its a set of representations of numerical values and symbols, including FP16, FP32, and FP64 (AKA Half, Single and Double-precision formats). FP32, for example, is broken down as a sequence of 32 bits, such as b31, b30, and b29, all the way down to zero.

A floating-point format is specified by a base (b), which is either 2 (binary) or 10 (decimal), a precision (p) range, and an exponent range from emin to emax, with emin = 1 emax for all IEEE 754 formats. The format comprises finite numbers that can be described by three integers.

These integers include s = a sign (zero or one), c = a significand (or coefficient) having no more than p digits when written in base b (i.e., an integer in the range through 0 to bp 1), and q = an exponent such that emin q + p 1 emax. The format also comprises two infinites (+ and ) and two kinds of NaN (Not a Number), including a quiet NaN (qNaN) and a signaling NaN (sNaN).

The details here are extensive, but this is the overall format of how the IEEE 754 floating-point functions; more detailed information can be found at the link above. FP32 and FP64 are on the larger floating-point spectrum, and theyre supported by x86 CPUs and most of today's GPUs, along with the C/C++, PyTorch, and TensorFlow programming languages. FP16, on the other hand, isn't widely used with modern processors, but its widely supported by current GPUs in conjunction with machine learning frameworks.

Bfloat16

Google's bfloat16 (Fig. 2) is another widely utilized floating-point format aimed at machine-learning workloads. The Brain Floating Point Format is basically a truncated version of IEEE's FP16, allowing for fast, single-precision conversion of the 754 to and from that format. When applied to machine learning, there are generally three flavors of values, including weights, activations, and gradients.

Google recommends storing weights and gradients in the FP32 format and storing activations in bfloat16. Of course, the weights also can be stored in BFloat16 without a significant performance degradation depending on the circumstances.

At its core, bfloat16 consists of one sign bit, eight exponent bits, and seven mantissa bits. This differs from the IEEE 16-bit floating-point, which was not designed with deep-learning applications in mind during its development. The format is utilized in Intel AI processors, including Nervana NNP-L1000, Xeon processors, Intel FPGAs, and Google Cloud TPUs.

Unlike the IEEE format, bfloat16 isnt used with C/C++ programming languages. However, it does take advantage of TensorFlow, AMD's ROCm, NVIDIA's CUDA, and the ARMv8.6-A software stack for AI applications.

TensorFloat

NVIDIA's TensorFloat (Fig. 3) is another excellent floating-point format. However, it was only designed to take advantage of TensorFlow TPUs built explicitly for AI applications. According to NVIDIA, "TensorFloat-32 is the new math mode in NVIDIA A100 GPUs for handling the matrix math also called tensor operations used at the heart of AI and certain HPC applications. TF32 running on Tensor Cores in A100 GPUs can provide up to 10X speedups compared to single-precision floating-point math (FP32) on Volta GPUs."

The format is just a 32-bit float that drops 13 precision bits to run on Tensor Cores. Thus, it has the precision of the FP16 (10 bits), but has the range of the FP32 (8 bits) IEEE 754 format.

NVIDIA states that TF32 uses the same 10-bit mantissa as the half-precision FP16 math, which is shown to have more than enough margin for the precision requirements of AI workloads. TF32 also adopts the same 8-bit exponent as FP32, so it can support the same numeric range. That means content can be converted from FP32 to TF32, making it easy to switch platforms.

Currently, TF32 doesnt support C/C++ programming languages, but NVIDIA says that the TensorFlow framework and a version of the PyTorch framework with support for TF32 on NGC are available for developers. While it limits the hardware and software that can be used with the format, its exceptional in performance on the companys GPUs.

This is just a basic overview of floating-point formats, an introduction to a larger, more extensive world designed to lessen hardware and software demands to drive innovation within the AI industry. It will be interesting to see how these platforms evolve over the coming years as AI becomes more advanced and ingrained within our lives. The technology is constantly evolving, so too must the formats that make developing machine-learning applications increasingly efficient in software execution.

See more here:
Floating-Point Formats in the World of Machine Learning - Electronic Design

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.

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

Image: LNER

Digital transformation is all about delivering change, so how do you do that in an industry that's traditionally associated with largescale infrastructures and embedded operational processes?

Danny Gonzalez, chief digital and innovation officer (CDIO) at London North Eastern Railway (LNER), says the answer is to place technology at the heart of everything your business does.

"We firmly believe that digital is absolutely crucial," he says. "We must deliver the experiences that meet or exceed customers' expectations."

Delivering to that agenda is no easy task. Gonzalez says the rail journey is "absolutely full" of elements that can go wrong for a passenger, from buying a ticket, to getting to the train station, to experiencing delays on-board, and onto struggling to get away from the station when they reach their destination.

SEE: Digital transformation: Trends and insights for success

LNER aims to fix pain points across customer journeys, but it must make those changes in a sector where legacy systems and processes still proliferate. Gonzalez says some of the technology being used is often more than 30 years' old.

"There's still an incredible amount of paper and spreadsheets being used across vast parts of the rail industry," he says.

"Our work is about looking at how things like machine learning, automation and integrated systems can really transform what we do and what customers receive."

Gonzalez says that work involves a focus on the ways technology can be used to improve how the business operates and delivers services to its customers.

This manifests as an in-depth blueprint for digital transformation, which Gonzalez refers to as LNER's North Star: "That gives everyone a focus on the important things to do."

As CDIO, he's created a 38-strong digital directorate of skilled specialists that step out of traditional railways processes and governance and into innovation and the generation of creative solutions to intractable challenges.

"It's quite unusual for a railway company to give more permission for people to try things and fail," he says.

Since 2020, the digital directorate in combination with its ecosystem of enterprise and startup partners has launched more than 60 tools and trialled 15 proof-of-concepts.

One of these concepts is an in-station avatar that has been developed alongside German national railway company Deutsche Bahn AG.

LNER ran a trial in Newcastle that allowed customers to interact in free-flowing conversations with an avatar at a dedicated booth at the station. The avatar plugged into LNER's booking engine, so customers could receive up-to-date information on service availability. Following the successful trial, LNER is now looking to procure a final solution for wider rollout.

The company is also working on what Gonzalez refers to as a "door-to-door" mobility-as-a-service application, which will keep customers up to date on the travel situation and provide hooks into other providers, such as taxi firms or car- and bike-hire specialists.

"It's about making sure the whole journey is seamlessly integrated," he says. "As a customer, you feel in control and you know we're making sure that if anything is going wrong through the process that we're putting it right."

When it comes to behind-the-scenes operational activities, LNER is investing heavily in machine-learning technology. Gonzalez's team has run a couple of impactful concepts that are now moving into production.

SEE:What is digital transformation? Everything you need to know about how technology is reshaping business

One of these is a technology called Quantum, which processes huge amounts of historical data and helps LNER's employees to reroute train services in the event of a disruption and to minimise the impact on customers.

"Quantum uses machine learning to learn the lessons of the past. It looks at the decisions that have been made historically and the impact they have made on the train service," he says.

Gonzalez: "We firmly believe that digital is absolutely crucial."

"It computes hundreds of thousands of potential eventualities of what might happen when certain decisions are made. It's completely transforming the way that our service delivery teams manage trains when there's disruption to services."

To identify and exploit new technologies, Gonzalez's team embracesconsultant McKinsey's three horizon model, delivering transformation across three key areas that allows LNER to assess potential opportunities for growth without neglecting performance in the present.

Horizon one focuses on "big, meaty products" that are essential to everyday operations, such as booking and reservations systems, while horizon two encompasses emerging opportunities that are currently being scoped out by the business.

Gonzalez says a lot of his team's activity is now focused on horizon three, which McKinsey suggests includes creative ideas for long-term profitable growth.

He says that process involves giving teams quite a lot of freedom to get on and try stuff, run proof of concepts, and actually understand where the technology works.

Crucial to this work isan accelerator called FutureLabs, where LNER works with the startup community to see if they can help push digital transformation in new and exciting directions.

"We go out with key problem statements across the business and ask the innovators to come and help us solve our challenges and that's led to some of the most impactful things that we've done as a business," says Gonzalez.

FutureLabs has already produced pioneering results. Both the Quantum machine-learning tool and the "door-to-door" mobility service have been developed alongside startup partners JNCTION and IOMOB respectively.

LNER continues to search for new inspiration and has just run the third cohort of its accelerator. Selected startups receive mentoring and funding opportunities to develop and scale up technology solutions.

Gonzalez says this targeted approach brings structure to LNER's interactions and investments in the startup community and that brings a competitive advantage.

"It's not like where I've seen in other places, where innovation initiatives tend to involve 'spray and pray'," he says. "The startups we work with are clear on the problems they're trying to solve, which leads to a much greater success rate."

SEE: Four ways to get noticed in the changing world of work

Gonzalez's advises other professionals to be crystal clear on the problems they're trying to solve through digital transformation.

"Know what the priorities are and bring the business along with you. Its really important the business understands the opportunities digital can bring in terms of how you work as an organisation," he says.

"We're fortunate that we've got a board that understood that rail wasn't where it needed to be in terms of its digital proposition. But we've put a lot of work into creating an understanding of where issues existed and the solutions that we needed if we're going to compete in the future."

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How avatars and machine learning are helping this company to fast track digital transformation - ZDNet

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

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Everything Youve Ever Wanted to Know About Machine Learning - KDnuggets