The sources said that Meta has started a small deployment of the custom chips and has plans to increase the production for wider usage if the experiment goes well. The company aims to deduct its huge infrastructure costs as it aims to invest heavily in AI development. In January 2025, Meta’s profits surged as Zuckerberg announced the company’s AI strategy.

Meta’s Push for AI Hardware Innovation.

With artificial intelligence leading the charge in technological development, businesses are increasingly turning towards bespoke hardware to maximize performance and minimize dependence on third-party chip manufacturers. Meta AI Training Chip is intended to facilitate the firm’s expanding AI operations, specifically in training intricate machine learning models for various platforms.

Meta forecasted its 2024 expenses to be $ 114 billion to $119 billion, out of which $65 billion is to be invested in AI infrastructure development. Last month, according to revenue analysts, Meta platforms are expected to thrive in 2025 in comparison to other tech giants like Microsoft and Amazon.

This is part of an overall plan by Meta to increase its AI abilities, with a focus on content moderation, recommendation systems, and generative AI for the metaverse. With the development of a Meta custom AI chip, the company wishes to improve efficiency while reducing energy consumption and operating expenses.

The sources said that Meta’s training chip is a dedicated accelerator as it is designed to handle only AI specific tasks unlike the integrated Graphic processing units. This makes them more powerful than the chips that are used to manage the AI workloads.

The Road to Full Deployment

Although Meta custom AI chip development is at an initial stage, the firm has already begun to test its features. The executives of the company said that the aim of the company is to start using its own AI chips by 2026 for training and compute-intensive process of feeding huge data to the AI systems.

Last week, Meta’s Chief Product Officer Chris Cox said at the Morgan Stanley technology, media and telecom conference, “We’re working on how would we do training for recommender systems and then eventually how do we think about training and inference for gen AI.”

Meta’s Step Ahead

Even with these developments, Meta is set to face strong competition from established players in AI chipmaking like Nvidia, Google, and AMD. These firms are already well-established in AI chip technology, which leaves one wondering how Meta’s bespoke AI chip will hold up in efficiency, scalability, and long-term.

Meta’s move to create an AI model training chip is a forward in AI hardware self-sufficiency. As AI technologies continue to get more complex, there will be an increased need for hardware solutions that are well-tuned for AI applications. Through the investment in Meta’s custom AI chip, the firm is establishing itself as a trailblazer in AI-driven technology.

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Everyone Wants Blackwell

CNBC reported that Nvidia AI chip demand is high among big techs like Meta, OpenAI, and Microsoft. Companies that are putting up AI data centers to power products like Copilot and ChatGPT have also placed orders. Blackwell chips will cost up to $40,000 per unit.

Speaking on CNBC’s Closing Bell Overtime, Huang said “Everybody wants to have the most and everybody wants to be first.”

Huang’s sentiments about next-gen AI chip demand resonate with comments made by some of Nvidia’s largest customers like Oracle’s Larry Ellison. In September 2024, Ellison said that he and Elon Musk had dinner with Huang where they begged Huang for more GPU chips

Blackwell Production on Course

Huang also talked about Blackwell AI chip production during the interview, saying that production is now on course and going as planned. In August 2024, Nvidia said the launch of Blackwell AI chips would be delayed for three or more months due to design flaws.

Huang also used the CNBC interview to reiterate that Nvidia is on track to deliver an efficient and faster GPU chip to its customers each year.

“If we can increase the performance as we’ve done with Hopper to Blackwell by two to three times each year, we’re effectively increasing the revenues or the throughput of our customers on these infrastructures by a couple of times each year, or you could think about it as decreasing costs every two or three years,” Huang said.

The Nvidia CEO emphasized that the company is working across the different layers of the computing stack, from software components to networking, and GPU chips

“This is a brand new way of doing computing, and we’re dedicated to build the entire stack and reinvent every layer of the technology stack so that every company in the world can benefit from this revolutionary new technology we call AI,” Huang said.

Riding the AI Boom

Nvidia is a major beneficiary of the AI boom. The value of Nvidia shares has increased by about 150% to date while its quarter two revenue hit $30.04 billion this year. The company expects sales revenue to hit the $32.5 billion mark this quarter.

“At a time when the technology is moving so fast, it gives us an opportunity to triple down, to really drive the innovation cycle so that we can increase capabilities, increase our throughput, decrease our costs, decrease our energy consumption. We’re on a path to do that, and everything’s on track,” Huang added.

Nvidia Blackwell AI chip demand is driven by their ability to train AI models at fast speeds while consuming less energy. The company expects to ship Blackwell products to Oracle, Google, Microsoft, and Amazon by the end of 2024.

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It is quite surprising how our food delivery apps show (suggest) us restaurants serving the kind of food which we would like to order. Isn’t it also fascinating how we can track the real-time locations of our Uber rides? Do you know what drives this technology? Buckle up as you’re about to find out the answer.

The facilitator is mobile machine learning or integration of machine learning in mobile apps.

Big tech companies use machine learning to create those interesting reactions in their mobile apps. In addition to the use of artificial intelligence in mobile applications, integrating machine learning is mainstream nowadays. But mobile machine learning is not a cakewalk. It is neither walking on eggshells. If you want to learn how to integrate machine learning into your mobile applications, then you are at the right place. Your next few minutes will be spent on reading (learning):

• Most common machine learning algorithms
• How to integrate machine learning into a mobile app development that is industry-specific
• Best machine learning examples and how they work

Before we move forward, let us take a glance at what machine learning is and why it should be integrated into mobile applications.

What Is Machine Learning?

When we speak of the present, we are already talking about yesterday’s future. Our present and the upcoming future are defined by technology—which further drives machines. It is rather pensive to think how machines are an important part of our life. A machine has to be very sophisticated to learn on its own any behavioral patterns that we subconsciously follow. It is These machines not only imitate us but also follow our patterns quite precisely. The major driver behind this is machine learning.

Machine learning is a branch or subset of artificial intelligence and computer science. It has the ability to automatically learn from the data without explicitly being programmed or assisted by domain expertise. ML focuses on the use of data and algorithms to imitate the way humans learn and gradually improves its accuracy.

How Is ML Beneficial?

Following are the benefits of integrating machine learning:

• 76% of businesses saw an increase in sales after ML integration
• ML technology predicts better user behavior, optimizes processes, and leads up-sell and cross-sell
• 50% of companies are using machine learning to improve their marketing strategies
• ML helped several European banks increase their product sales by 10%
• Let us now focus on the various types of machine learning algorithms available for Android or iOS apps.

“The more precise ML algorithms are made with more data, the better.”

Machine Learning employs the following algorithms to build models that reveal connections:

• Supervised learning: When an algorithm learns by using example data and the associated target responses. These data could include numeric or string labels, such as classes and tags. ML can then predict the correct answer if presented with new examples.
• Unsupervised learning: ML learns by looking at examples and not having to look for answers. The algorithm thus determines data patterns by itself.
• Reinforcement Learning: Developers train ML algorithms so that they can make certain decisions from the environment. This allows the machine to capture the most accurate knowledge possible and make precise decisions.

Use of Machine Learning in Specific Industries

Machine Learning has various applications. It can be used in different industries to create mobile apps. We have noted down some ML use cases in mobile apps that are industry-specific.

1. AI-Powered Financial Assistant

Let us understand how ML is used for financing. You can use various mobile apps to gain insights into your finances. These apps are usually developed by banks to offer clients added value. They use machine learning algorithms to analyze transaction history, predict future spending, track spending patterns, and provide financial advice to users. For instance, Erica is a mobile voice assistant developed by Bank of America. Over Erica’s financial assistant Erica, Currency offers more personal and convenient banking for 25 million mobile app users.

2. Mobile Fitness Apps With ML

Various workout apps, powered by machine learning, analyze data from smartwatches, wearables, and fitness trackers. Based on their user’s goals, they receive personalized lifestyle advice. To create customized fitness plans, the algorithm analyzes user’s current health and eating habits. One of the most popular fitness apps that use machine learning is Aptiva coach. It offers a variety of workouts and even custom Aptiva workouts. The app also tracks user progress.

3. Healthcare Mobile Applications for Healthcare With ML

Many condition-based mobile apps make it easy to track heart diseases, diabetes, epilepsy, migraines, and other conditions. These apps use machine learning algorithms to analyze user input and predict possible conditions. They also notify doctors about current conditions for faster treatment.

4. Transport Mobile Apps

Mobile apps for logistics, such as Uber Trucking or Fleet Management, must provide drivers with current information on traffic conditions. These apps then optimize roads based on current conditions to avoid traffic jams and deliver cargo on time. Developers integrate machine learning algorithms with traffic prediction software into road optimization mobile applications to receive this traffic information before it happen. This algorithm analyzes historical traffic data and predicts traffic patterns for a specific day and time. Learn more about machine learning applications in transportation by reading the article How AI is changing logistics.

5. E-commerce

Machine Learning algorithms can be used in a variety of ways by online retail mobile apps. These algorithms can be used to offer more relevant product recommendations to buyers based on their purchase history, credit card fraud identification, and visual search. You can find more machine learning applications in mobile eCommerce apps by reading the article on how online apparel retailers can leverage AI to sell online.

5 Common Examples of Mobile Machine Learning Integration

Innovative algorithms improve the user experience on their mobile devices and bring new machine-learning mobile app ideas. Below is a list of the top machine-learning apps.

1. Snapchat

This application uses machine-supervised learning algorithms for computer visualization. The algorithm for computer vision was developed by Looksery, a Ukrainian startup. This company was soon acquired by Snapchat for $150 million. The mobile machine learning algorithm uses photos to find faces and add fun elements such as glasses, hats, ears, and more. We have provided a detailed explanation of how ML Snapchat filters operate in this article.

2. Yelp

The app uses supervised machine learning to improve user experience by recommending “Recommended For You” collections. The ML algorithm reviews each restaurant. The ML algorithm then determines which dishes are most popular based on how often the meal has been mentioned. Yelp also uses ML to collect, classify and label user-submitted photographs of dishes with different attributes. These attributes include “ambiance is elegant” and “good with children” with 83% accuracy.

3. Facebook

Facebook uses machine learning algorithms in many ways. After the ml algorithm has analyzed your profile, interests, current friends, and their friends, Facebook suggests new friends to you in the “People You May Know”. The algorithm can also pull in other factors to suggest people you might know. Facebook also uses machine learning in Newsfeed, targeted ads, and facial recognition.

4. Netflix

Netflix uses machine learning algorithms. It has incorporated precise, personalized references by using linear regression and logistic regression along with other similar algorithms. Netflix’s mobile app uses a diverse range of content based on variety, actors, user and critics’ reviews, and much more for its audience. This information is studied by machine learning algorithms.

In the case of Netflix, ML algorithms are trained by user actions that track users’ behavior. These algorithms study what TV shows are mostly watched by users and the type of reviews received online. These algorithms are familiar with user behaviors and hence offer exceedingly personalized content.

5. Google Maps

Interestingly, Google Maps also utilizes machine learning algorithms to gather and study data from a very large number of people. Researchers on Google ask questions like how long it takes for commuting or if they face any difficulty to find vehicle parking. They derive, aggregate, and use this information by creating various training models from people who have shared their location information.

Final Thoughts: Machine Learning and Mobile Apps

Machine learning algorithms can improve customer experience, loyalty, engagement, and similar aspects. It is very suitable for any mobile app that requires predictions and leverages enough data.

Today, machine learning has numerous applications, from banking to healthcare. Depending on the needs of your business, you may be able to leverage any one of these ML algorithms. Last but not least, you need to hire an experienced team to develop machine learning apps.

The post Top 5 Examples of Machine Learning App Development appeared first on Tech Research Online.

Artificial Intelligence and Machine Learning are the buzzwords of the tech world. Since both the terms are based on statistics and math’s, people often get confused between them.

Today, no conversation on technology is complete without the words Artificial Intelligence and Machine Learning. These two have become buzzwords, at the top of advancements in intelligent systems and software, frequently popping up in more broad domains like Big Data and Analytics.

But, don’t worry! In this blog, we will cover the major differences between artificial intelligence and machine learning to eliminate this confusion. However, before we proceed with learning the differences, let me help you solidify the broader understanding of what artificial intelligence and machine learning are.

What is Artificial Intelligence?

Artificial Intelligence is a technology that allows computers and machines to perform various processes of perception, thinking, understanding, analysis, and others. This futuristic technology aims to ease human effort by doing all day-to-day tasks.

It’s creating virtual personal assistants and intelligent conversational systems as well as more complex systems in healthcare, finance, and self-driving vehicles. AI is no longer about finishing up the chores efficiently but also about opening prospects that address complicated problems of the world. The devices that are equipped with AI are continuously evolving with newer developments coming in every day..

Apart from just being useful, AI is also driving innovation in new areas of application of the technology including predictive analysis, robotics, and experiences. For instance, AI in customer success is bringing in a paradigm shift in how companies approach their clients. Also, keeping up with the AI trends helps organizations stay ahead of the curve in capitalizing on this technology.

What is Machine Learning?

Machine Learning (ML) is a new field of Artificial Intelligence that enables systems to improve their ability to learn and adapt from past experiences on their own without additional coding. It has also contributed to the foundation of many practical applications that include using recommender systems in streaming services, reducing fraud risks in the financial industry, using predictive analytics in manufacturing to determine equipment’s state, and applying natural language processing algorithms in chatbots and voice assistants.

Machine Learning app development presents a way in which business apps can be customized. Besides, it is necessary to understand how to use the proper ML platform to create, implement, and scale many models across industries to guarantee sustainable prosperity in the future. Though Machine Learning is still developing, it is opening doors to ideas such as self-driving cars, real-time translation, and detailed climate prediction.

Difference Between Artificial Intelligence and Machine Learning

Artificial Intelligence (AI) and Machine Learning (ML) are related fields but perform different functions. AI is a broad concept where machines are designed to imitate human intelligence, including reasoning, decision-making, and problem-solving. It includes a range of technologies aimed at enabling machines to perform tasks typically requiring human intelligence. On the other hand, ML is a subset of AI that enables machines to learn from data and improve their performance over time without explicit programming. Listed down below are a few major differences between AI and ML.

5 Benefits of Artificial Intelligence and Machine Learning

From organizational efficiency to better creativity, the benefits of Artificial Intelligence and Machine Learning are changing industries and personal lives. In 2024 and beyond, these technologies will promote growth at a higher rate. Industries that have seen productivity improvements include healthcare, manufacturing, and marketing. Business leaders have estimated an increase in efficiency up to 73% through the application of ML.

Here is the list of 5 benefits that industries could get by the application of AI and ML:

• Improved Decision-Making: AI and ML analyze huge amounts of data and provide valuable insights to support smarter and faster decision-making.
• Enhanced Efficiency and Automation: By automating repetitive tasks, AI and ML free up human resources for more strategic activities.
• Personalized Experiences: ML-powered systems adapt to individual preferences, enabling hyper-personalized recommendations across all platforms.
• Predictive Analytics: AI and ML excel at forecasting trends, detecting anomalies, and predicting outcomes in various industries.
• Cost Savings and Revenue Growth: By optimizing processes, reducing errors, and enhancing customer satisfaction, AI and ML help in cost saving.

Examples of Artificial Intelligence and Machine Learning

Artificial Intelligence and Machine Learning are everywhere from home appliances to office laptops. Moreover, with the Internet of Things like Alexa and smart watches entering our lives, we are dependent on AI and ML more than ever. Here are the 3 examples of the usage of these technologies:

3 Common Examples of AI Incorporation

AI has become an integral part of many aspects of our life, changing the way we relate to technology and the world at large. From mundane applications to transforming entire industries, AI is no longer a futuristic idea but a reality for today. Its strength lies in enhancing personalization, improving efficiency, and driving innovation across sectors. Following are some of the notable instances of AI incorporation:

1. Personalized AI Assistants

Alexa by Amazon, Siri by Apple, S Voice by Samsung, Cortana by Microsoft, and Google Assistant. All of these are perfect and most popular examples of personalized AI assistants. These tools have enabled human interactions with gadgets and have enabled us to do a plethora of things from hotel bookings to window shopping.

2. Robotics

AI robots are another example of AI integration. Think of the world’s first AI humanoid robot, Sophia, who is incorporated with artificial intelligence. Her creators claim that Sophia personifies their dreams for the future of AI. Sophia can also initiate conversations on a variety of predefined topics. In fact, AI robots have a keen role to play in the future.

3. Marketing

AI has a great role to play in facilitating the future of marketing. With tools like Slack and Grammarly, today marketers are allocating huge amounts of financing towards incorporating AI in their marketing tactics.

Now that we have learned about AI and its examples in a brief manner, let us move forward to understanding Machine Learning in depth.

3 Common Examples of Machine Learning

Machine learning (ML) has become an integral part of many industries, changing the way we interact with technology and make decisions. From healthcare to finance, ML’s applications are broad and expanding day by day. As technology develops, so does its potential to enhance efficiency, accuracy, and decision-making. Below is a list of examples where Machine Learning is used vastly:

1. Image and Speech Recognition

Image recognition is a widespread example of ML. It helps identify an object as a digital image, based on the intensity of the pixels in black-and-white images or color images. For example, labeling an x-ray, assigning a name to a photographed face, recognizing handwriting, and many more.

ML is also used for facial recognition within an image in which using a database of people, the system identifies commonalities and matches them to faces.

2. Medical Diagnosis

In the past few years, Machine Learning has played a significant role in the diagnosis of diseases. It assists in formulating a diagnosis or recommending treatment options that require the incorporation of ML. In fact, oncology and pathology also use machine learning to recognize cancerous tissues and analyze body fluids.

3. Data Extraction

ML helps extract structured information from unstructured data. Several organizations collect huge chunks of data from customers and using ML algorithm, they automate the process of annotating datasets for predictive analytics tools. Examples: Generating models to predict vocal cord disorders, developing methods for prevention, diagnosis, and treatment of disorders, and many more.

Conclusion: The Future of AI & ML

As Artificial Intelligence and Machine Learning continue to grow, they unlock new opportunities, change processes, and improve decision-making capabilities. Industries are increasingly adopting these innovations to improve efficiency and stay competitive, making AI and ML as central forces shaping the future of business. Ranging from transforming the concepts of healthcare and education to speaking of sustainability and automation, their ability to solve multifaceted problems is making a breakthrough impact across numerous companies and societies.

They are allowing developers to build better systems, work more effectively, and provide solutions that would have been unimaginable before. There will be more focus on relevant issues such as responsible AI & ML and information privacy.

Increased regulation and control over the use of AI and ML will become a necessity in the future after learning about their benefits. These technologies are mandatory for the betterment of future intelligence that is characterized by more strong, updated, and closely integrated applications.

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