Top 10: AI Platforms

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Top 10 AI Platforms
Mobile Magazine considers some of the pioneering AI platforms that support the industry as it continues to digitally transform with disruptive technology

As those in the mobile industry are getting to grips with disruptive technologies, AI has fast-emerged as an innovative solution. Transforming the way users interact with their smartphones, the technology is able to enable intelligent features and bolster personalised user experiences, provide intelligent virtual assistants and advance image and speech recognition. 

As AI platforms continue to evolve within the business world, many have been touted as a way to drive innovation in telecommunications. The technology has the capability to improve network infrastructure and user-facing applications. 

Mobile Magazine therefore provides a list of some of the most innovative AI platforms currently creating global impact.

10. TensorFlow

  • Revenue: US$307.39bn (Parent company Alphabet)

  • Number of employees: 182,000+

  • CEO: Sundar Pichai

  • Year founded: 1998

Sundar Pichai, Chief Executive Officer leads the development of TensorFlow, enhancing machine learning capabilities

TensorFlow is a free and open-source AI and machine learning framework developed by Google Brain. It can be used across a range of tasks, but has a specialised focus on training and inference of deep neural networks. It was initially developed by Google Brain for Google’s internal use in research and production.

The platform provides a flexible ecosystem of tools and community resources for building and deploying machine learning models. It supports both deep learning and traditional machine learning algorithms.

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9. Anthropic’s Claude

  • Revenue: US$850m (forecast)

  • Number of employees: 1,000+

  • CEO: Dario Amodei

  • Year founded: 2021

Dario Amodei, Chief Executive Officer of Anthropic introduces Claude, setting a new standard in AI performance

Anthropic, the company behind Claude, is committed to developing reliable AI systems and conducting research on AI opportunities and risks. Claude 3, their most recent model, offers improved performance and longer response capabilities. It excels in various tasks, which range from engaging in sophisticated dialogue to generating creative content and following detailed instructions. Given that the models can understand more context, Anthropic says that they can therefore process more information.

In addition, Anthropic also offers Claude Instant, which is a model more capable of handling a range of tasks such as casual conversation, text analysis, summarisation and document comprehension.

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8. Adobe Sensei

  • Revenue: US$19.41bn (2023)

  • Number of employees: 29,000+

  • CEO: Shantanu Narayen

  • Year founded: 1982

Shantanu Narayen, Chief Executive Officer highlights Adobe Sensei's transformative impact on creative workflows

Adobe's Sensei platform harnesses the power of cloud AI and machine learning to enhance creative experiences. It is designed to deepen insights, boost creative expression, streamline tasks and workflows, in addition to enabling real-time decision-making. Adobe has also recently announced several Gen AI innovations across its Experience Cloud, which are set to redefine how businesses deliver customer experiences.

Sensei’s Gen AI utilises multiple large language models (LLMs) within the Adobe Experience Platform and can adapt itself to suit the unique needs of a business.

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7. NVIDIA AI

  • Revenue: US$3.34bn (market cap)

  • Number of employees: 26,000+

  • CEO: Jensen Huang

  • Year founded: 1993

Jensen Huang, Chief Executive Officer showcases how NVIDIA AI drives advanced GPU-accelerated solutions for developers

NVIDIA AI is a comprehensive platform that leverages Nvidia’s expertise in GPU acceleration to power AI development and deployment. The platform includes hardware solutions like NVIDIA GPUs and DGX systems, in addition to software tools for parallel computing and deep neural networks.

The company’s AI platforms often excel in areas requiring intense computational power, such as computer vision, natural language processing and recommender systems. In addition, its AI thrives when it comes to supporting AI that is required for real-time use cases such as autonomous vehicles, robotics and high-performance computing.

At the start of 2024, Nvidia partnered with Nokia with the goal of transforming mobile networks through AI-ready radio access network (RAN) solutions.

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6. IBM Watson

  • Revenue: US$62bn (2023)

  • Number of employees: 310,000+

  • CEO: Arvind Krishna

  • Year founded: 1911

Arvind Krishna, Chief Executive Officer illustrates IBM Watson's effectiveness in delivering AI solutions across industries

IBM Watson is a suite of enterprise-ready AI services, applications and tools designed to bring AI capabilities to businesses across a broad range of industries like healthcare, finance and retail. It can understand, reason and learn from data and interactions, as well as integrate with existing systems for specific requirements.

It offers pre-built APIs for natural language processing, speech-to-text, text-to-speech and computer vision tasks. Watson's strengths lie in its ability to process and analyse unstructured data, making it particularly useful for tasks like sentiment analysis, content recommendation and knowledge discovery.

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5. OpenAI’s ChatGPT

  • Revenue: US$3.4bn

  • Number of employees: 3,400+

  • CEO: Sam Altman

  • Year founded: 2015

Sam Altman, Chief Executive Officer presents ChatGPT, an innovative tool reshaping natural language processing

Taking the world by storm in 2022, ChatGPT was created by OpenAI as an AI-powered natural language processing tool that enables users to have human-like conversations with a chatbot. The AI platform can respond to inquiries and support users in a range of tasks from composing emails and essays to writing code - with its paid-for service able to generate images and more sophisticated responses to prompts.

ChatGPT has been trained from a huge amount of text from the internet and is now used by countless businesses for chatbot, virtual assistant and customer support services. It also had multibillion-dollar support from tech giant Microsoft.

4. Microsoft Azure AI

  • Revenue: $211.92 billion

  • Number of employees: 221,000 (as of 2023)

  • CEO: Satya Nadella

  • Year founded: 1975

Satya Nadella, Chief Executive Officer emphasises the extensive capabilities of Microsoft Azure AI in various sectors

Microsoft Azure AI is a comprehensive cloud-based platform offering a wide range of AI and machine learning services. It enables building, training and deploying AI models at scale that integrate seamlessly with other Microsoft services. Additionally, Azure AI provides pre-built models for computer vision, natural language processing and speech recognition, which ultimately saves development time.

Azure AI powers virtual agents for customer service, assists in anomaly detection for fraud and cybersecurity and enables personalised marketing campaigns through customer behaviour analysis. The platform’s strengths lie in its enterprise-grade security and its ability to handle multi-cloud scenarios.

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3. Amazon SageMaker (AWS)

  • Revenue: US$574.79bn

  • Number of employees: 130,000+

  • CEO: Andy Jassy

  • Year founded: 2006

Andy Jassy, Chief Executive Officer highlights the efficiency of Amazon SageMaker in streamlining machine learning processes

Amazon SageMaker, a fully managed machine learning service by Amazon Web Services (AWS), is ranked as one of the top AI tools in 2024. It simplifies the entire machine learning workflow, from data preparation and model training to deployment and monitoring, offering a scalable and cost-effective solution for enterprises to build and deploy AI applications.

Streamlining the development process, it offers tools for model optimisation, where teams can collaborate, share and experiment with models using SageMaker, fostering innovation and productivity.

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2. Apple's Core ML

  • Revenue: US$383.2bn (2023)

  • Number of employees: 164,000+

  • CEO: Tim Cook

  • Year founded: 1976

Tim Cook, Chief Executive Officer shares the importance of Core ML in integrating seamless AI experiences across devices

Core ML is Apple’s machine learning framework that is designed to integrate AI capabilities into iOS, macOS, watchOS and tvOS applications. It focuses on on-device machine learning, prioritising user privacy and reducing reliance on cloud processing.

The AI supports a broad range of model types, including neural networks, and is optimised for Apple hardware by leveraging the neural engine in Apple silicon and minimising memory footprint and power consumption. Developers can convert models from popular frameworks like TensorFlow and scikit-learn to Core ML format.

Core ML excels in tasks such as image analysis, natural language processing and sound analysis. Its integration with Apple's ecosystem allows for seamless implementation of AI features in apps, ensuring high performance and energy efficiency on Apple devices.

By leveraging Core ML, developers can create intelligent features and enable new experiences in their apps, taking advantage of powerful on-device machine learning capabilities across Apple's ecosystem.

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1. Google’s Gemini AI

  • Revenue: US$307.39bn (Parent company Alphabet)

  • Number of employees: 182,000+

  • CEO: Sundar Pichai

  • Year founded: 1998

Sundar Pichai, Chief Executive Officer elaborates on Gemini AI’s ability to integrate multi-modal capabilities into applications

Gemini is a family of AI models created by tech giant Google. It is designed to help developers build, deploy and manage AI models at scale, in addition to integrating seamlessly with other Google Cloud services.

As a multimodal AI model, Gemini can process and understand information from multiple sources, including images, video, audio, text and code. It can also transform any type of input into any type of output.

First created as Bard in early 2023, the model had moved Bard to PaLM 2, a new language model - and then refined and almost exactly a year later rebranded to Gemini. Now, Gemini seeks to use a mix of database learning, compiled with live information, to provide its users with holistic and accurate answers to their queries.

The platform's strengths lie in its integration with Google's vast data ecosystem and its ability to handle large and complex AI projects.

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To read the full story in the magazine click HERE


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