Tag: Artificial Intelligence

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Nvidia blasts off into AI superstardom

AI rocket

Technology giant Nvidia reports its sales have hit a record after more than doubling as demand for its artificial intelligence (AI) chips take off!

It figures

The company says revenue jumped to above $13.5bn (£10.6bn) for the three months to the end of June. Nvidia also expects sales to perform very well in the current quarter and plans to buy back $25bn of its stock. The firm’s shares rose by more than 6.5% in extended trading in New York, adding to their huge gains this year. Nvidia also said it expects revenue of around $16bn for the three months to the end of September 2023.

That is substantially higher than Wall Street expected and would equate to a rise of around 170%, compared to the same time last year.

Even before 23rd August’s figures, Nvidia’s stock price had more than tripled for the year, making it the top performer in the S&P 500. It’s share price jumped to around $500 after hours, a level that would mark a record if it closes there on 24th August 2023. Its prior closing high was $474.94 on 18th July 2023.

‘A new computing era has begun’, Nvidia’s chief executive, Jensen Huang, said in a statement. ‘Companies worldwide are transitioning from general-purpose to accelerated computing and generative AI’, he reportedly added.

Strong performance

The strong performance was driven by Nvidia’s data centre business, which includes AI chips.

The power of Nvidia’s AI microchips

Revenue for that unit came in at more $10.3bn, a rise of more than 170% from year ago, as cloud computing service providers and large consumer internet companies snapped up its next-generation processors.

This year, Nvidia’s stock market value has jumped to more than $1 trillion as its shares more than tripled in value, mking it the fifth publicly traded U.S. company to join the so-called ‘Trillion dollar club’, along with Apple, Amazon, Alphabet and Microsoft.

AI ‘mania’ helps Nvidia

Nvidia have been making micro chips for a long time and it’s only really been in the last couple of years that the market has caught on.

Nvidia was originally known for making the type of computer chips that process graphics, particularly for computer games. They have been making chips for a long time and have now become the leader in AI chip design and manufacture.

Now Nvidia’s hardware is the foundation for most AI applications, with one report suggesting it had cornered 95% of the market for machine learning.

ChatGPT which generates human-like responses to user queries within seconds was trained using 10,000 of Nvidia’s graphics processing units clustered together in a supercomputer belonging to Microsoft.

AI products are expected to dramatically change how we use computers and the role they play in our lives.

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Hundreds caught breaking the law on AI camera system in Cornwall UK

AI camera Surveillance

A new artificial intelligence (AI) road safety camera system has been deployed on the A30 near Launceston, Cornwall, by Devon and Cornwall Police.

The camera system uses AI to detect potential offences such as using mobile phones or not wearing seatbelts while driving. The camera system can capture ultra clear images of the car’s interior and send them to a human reviewer who can issue a warning letter or a notice of intended prosecution, depending on the severity of the offence. 

Hundreds caught

The camera system is the first of its kind to be used in the UK and it has caught almost 300 drivers breaking the law in the first three days of its operation. The camera system is part of the Vision Zero South West project, which aims to reduce road deaths and serious injuries in Devon and Cornwall. The project conducted a 15-day trial of a similar vehicle-based system last year and detected 590 seat belt and 45 mobile phone offences across various roads in the two counties. 

The police hope that the new technology will help deter drivers from using their phones or not wearing their seatbelts, which are both dangerous and illegal behaviours that put people’s lives at risk.

The road safety system is from tech’ firm Acusensus.

The police have reportedly said they are ‘disappointed’ by the number of people not wearing seatbelts orand using their mobile ‘phones when drving.

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Amazon – leading or competing?

The power of AI

Amazon is one of the leading companies in the field of artificial intelligence (AI) and has been developing its own custom chips to power its AI applications and services.

Amazon’s AI chips are designed to perform tasks such as natural language processing, computer vision, speech recognition, and machine learning inference and training.

AI chips created by Amazon

  • AZ2: This is a processor built into the Echo Show 15 smart display and powers artificial intelligence tasks like understanding your voice commands and figuring out who is issuing those commands. The AZ2 chip also enables features such as visual ID, which can recognize faces and display personalized information on the screen.
  • Inferentia: This is a high-performance chip that Amazon launched to deliver low-cost and high-throughput inference for deep learning applications. Inferentia powers Amazon Elastic Compute Cloud (EC2) Inf1 instances, which are optimized for running inference workloads on AWS. Inferentia also powers some of Amazon’s own services, such as Alexa, Rekognition, and SageMaker Neo.
  • Trainium: This is a chip that Amazon designed to provide high-performance and low-cost training for machine learning models. Trainium will power Amazon EC2 Inf2 instances, which are designed to train increasingly complex models, such as large language models and vision transformers. Trainium will also support scale-out distributed training with ultra-high-speed connectivity between accelerators.

Despite advancements is Amazon chasing to keep up?

Amazon is racing to catch up with Microsoft and Google in the field of generative AI, which is a branch of AI that can create new content or data from existing data. Generative AI can be used for applications such as natural language generation, image and video synthesis, text summarization, and personalization.

AI models from Amazon

  • Titan: This is a family of large language models (LLMs). Titan models can generate natural language texts for various domains and tasks, such as conversational agents, document summarization, product reviews, and more. Titan models are trained on a large and diverse corpus of text data from various sources, such as books, news articles, social media posts, and product descriptions.
Power of AI
Powerful chips for artificial intelligence (AI)
  • Bedrock: This is a service that Amazon created to help developers enhance their software using generative AI. Bedrock provides access to pre-trained Titan models and tools to customize them for specific use cases. Bedrock also allows developers to deploy their generative AI applications on AWS using Inferentia or Trainium chips.

Generative AI

Amazon’s CEO, Andy Jassy in the past said he thought of generative AI as having three macro layers: the compute, the models, and the applications. He said that Amazon is investing heavily in all three layers and that its custom chips are a key part of its strategy to provide high-performance and low-cost compute for generative AI. He also said that Amazon is not used to chasing markets but creating them, and that he believes Amazon has the best platform for generative AI in the world.

Inferentia and Trainium, offer AWS customers an alternative to training their large language models on Nvidia GPUs, which have been getting difficult and expensive to procure. 

‘The entire world would like more chips for doing generative AI, whether that’s GPUs or whether that’s Amazon’s own chips that we’re designing’, Amazon Web Services CEO Adam Selipsky is reported to have said. ‘I think that we’re in a better position than anybody else on Earth to supply the capacity that our customers collectively are going to want’.

Fast actors

Yet others have acted faster, and invested more, to capture business from the generative AI boom. When OpenAI launched ChatGPT in November 2022, Microsoft gained widespread attention for hosting the chatbot, and investing a reportedly whopping $13 billion in OpenAI. It was quick to add the generative AI models to its own products, incorporating them into Bing in February 2023. 

That same month, Google launched its own large language model, Bard, followed by a $300 million investment in OpenAI rival Anthropic. 

Chat Bot
AI Chat Bot robot

It wasn’t until April 2023 that Amazon announced its own family of large language models, called Titan, along with a service called Bedrock to help developers enhance software using generative AI.

Amazon is not used to chasing markets. Amazon is used to creating markets. And for the first time for some time, they find themselves on the back foot and working to play catch up.

And Meta?

Meta also recently released its own LLM, Llama 2. The open-source ChatGPT rival is now available for people to test on Microsoft’s Azure public cloud.

The AI battle continues…

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AI race gathers momentum as China’s Baidu claims its Ernie Bot is Better than ChatGPT on key tests

AI Robots Chatting

Baidu said its AI system called Ernie 3.5 outperformed OpenAI’s ChatGPT and GPT4 in several key areas.

  • The Chat Bot was revealed in March 2023 and has since been publicly testing it in China. The chatbot is based on Baidu’s foundational AI model called ERNIE.
  • Baidu’s advancements underscore the intense competition taking place in the area of generative AI with technology giants in the US and China rapidly advancing their AI models.

 ERNIE – Enhanced Language RepresentatioN with Informative Entities

US and China AI Bots go head to head

Ernie was first introduced in 2019, and since then, Baidu has been improving and upgrading it with new versions. The latest version, Ernie 3.5, was announced in June 2023, and it claims to outperform OpenAI’s ChatGPT and GPT 4 in several key areas

Baidu’s Ernie is an artificial intelligence (AI) model that powers the company’s chatbot service, Ernie Bot. Ernie stands for Enhanced Language RepresentatioN with Informative Entities, and it is a natural language processing (NLP) deep-learning model that can understand and generate natural language.

Trained on large data sets

Ernie 3.5 is based on Baidu’s foundational AI model, which is trained on huge amounts of data from various domains, such as news, social media, encyclopedias, books, and more. Ernie 3.5 can handle various NLP tasks, such as question answering, dialogue generation, text summarization, sentiment analysis, and more.

According to a test by the China Science Daily journal, Ernie 3.5 surpassed ChatGPT and GPT 4 in general abilities and outperformed the more advanced GPT 4 on several Chinese-language capabilities. 

ERNIE version 3.5 boosted its training and efficiency, making it faster and cheaper to upgrade to future versions. Baidu hopes that ERNIE Bot will become the next must-have app in China’s internet market, attracting users because of its natural and engaging conversations.

Intergration

Baidu has been integrating ERNIE Bot across multiple business applications, ranging from cloud computing to smart speakers. 

Chat Bot
AI Chatbot

ERNIE Bot is one of the examples of how Baidu is investing in AI technology and competing with other tech giants in the US and China. Baidu’s founder Robin Li, reportedly said that ‘foundation models are an engine driving global economic growth and represent a major strategic opportunity that cannot be missed‘.

The major BIG players, Alphabet (Google), Microsoft & META all have their own versions of AI. Hopefully it will be used ‘intelligently’.

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What is AI?

AI stands for Artificial Intelligence, which is the ability of machines to perform tasks that normally require human intelligence. AI can involve various aspects of cognition, such as perceiving, reasoning, learning, problem-solving, and even creativity.

Types of AI

AI can be classified into different types based on the level of intelligence and the scope of tasks that machines can perform. One common way to categorise AI is by using the following four types:

  • Narrow AI: This is the most common and basic type of AI, which refers to machines that can perform specific tasks very well, but cannot generalize to other domains or situations. Examples of narrow AI include speech recognition, face recognition, web search engines, recommendation systems, self-driving cars, and chess-playing programs.
  • General AI: This is the type of AI that aims to achieve human-like intelligence across a wide range of domains and tasks. General AI would be able to understand and learn from any kind of data, reason and solve problems, communicate and interact with humans, and exhibit common sense and creativity. However, general AI does not exist yet, and it is considered a long-term goal of AI research.
  • Super AI: This is the type of AI that would surpass human intelligence in all aspects, including speed, memory, knowledge, creativity, and wisdom. Super AI would be able to outperform humans in any task and domain, and potentially pose existential risks to humanity. Super AI is also a hypothetical concept that has not been achieved or proven yet.
  • Artificial neural networks: This is a type of AI that mimics the structure and function of biological neural networks, which are the basis of human brain activity. Artificial neural networks consist of layers of interconnected nodes that process information and learn from data. Artificial neural networks are widely used for various applications of AI, such as computer vision, natural language processing, machine learning, and deep learning.

Applications of AI

AI has many applications in various fields and industries, such as:

  • Healthcare: AI can help diagnose diseases, analyze medical images, design drugs, assist surgeries, monitor patients, and provide personalized care.
  • Education: AI can help personalize learning, assess students’ progress, provide feedback, tutor students, grade assignments, and create educational content.
  • Business: AI can help optimize operations, enhance customer service, automate tasks, analyze data, generate insights, improve decision making, and increase productivity.
  • Entertainment: AI can help create music, art, games, movies, and other forms of entertainment.
  • AI can also help recommend content to users based on their preferences and behaviour.
  • Security: AI can help detect threats, prevent cyberattacks, enhance surveillance, identify frauds, enforce laws, and protect privacy.
  • Social: AI can help communicate with humans through natural language processing and speech synthesis. AI can also help understand human emotions and behavior through sentiment analysis and facial expression recognition.

Challenges and limitations of AI

AI also faces many challenges and limitations that need to be addressed by researchers and users. Some of these include:

  • Ethical: AI raises many ethical issues such as bias, fairness, accountability, transparency, privacy, human dignity, and social impact. How can we ensure that AI is aligned with human values and does not harm humans or society?
  • Technical: AI still faces many technical challenges such as scalability, robustness, explainability, interpretability, generalization, and adversarial attacks. How can we improve the performance, reliability, and security of AI systems?
  • Societal: AI also has many societal implications such as employment, education, regulation, governance, and collaboration. How can we adapt to the changes and opportunities that AI brings to our lives, work, and society?

AI is here to stay – it’s all about how we use it for the betterment of ‘humankind’. Please, let’s use it safely, responsibly and for the good!

Big tech companies heavily involved in the development of AI products

OpenAI

Microsoft

Alphabet/Google

Amazon

Nvidia

AMD

Arm

Meta