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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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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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.

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Floating-Point Formats in the World of Machine Learning - Electronic Design