🤖 Intro
Hello, welcome to part 4 of the 2023 Business trends series where we’ll be unpacking the AI and generative AI business trends, which includes ML, Deep learning, NLP, etc, as sub-fields of AI and AI research more broadly.
We’ll kick things off with an overview of the generative AI application landscape. Moving on, we will review market research for AI and ML, Artificial intelligence companies to watch in 2023, and conclude with Areas of AI expected to grow in 2023.
Despite falling under the Technology industry, Artificial Intelligence and Machine Learning are so broad in their application and scope of capabilities that they deserve their own 2023 business trends post because of the significant market opportunities they represent, and their impact on nearly every industry, sector, and business unit.
| ✴️ Goals for this post: As a recap, the primary objective of the Business Trends series has been to provide resources that both product teams and business leaders can use to support product and business strategy planning in 2023 and beyond. As such, this post can also be used to introduce those who possess less knowledge and understanding of the AI landscape but will be equally as beneficial to those who already have a thorough understanding of the technologies. |
⚙️ Table-Of-Contents
🌐 The Generative (AI) Application Landscape - Understanding the latent space of meaning
GPT-3, Dall-E, and now Stable Diffusion is revolutionizing how we approach real-world problems with artificial intelligence. These models’ unparalleled accuracy and versatility make them capable of grasping the latent space of meaning and performing tasks like writing and image generation that were unimaginable just a few years ago.
Aside from changing the way we interact with the world, this new capability has created a new way to access and compress knowledge. The internet and knowledge have been abstracted, and change is expected to accelerate exponentially in 2023; As we’ve reached the point with these technologies where we can’t distinguish the machines from humans on an emotional level, the newest generation of models is advancing towards emotional realism.
💹 AI Market Research
AI and ML will reach ~ $122.5 billion USD by 2022, at a compound annual growth rate of 37.7%. During 2023, the market is expected to reach ~ $166 billion USD. As a result, this would represent a compound annual growth rate of 21.4%. According to Stratview Research, the market is expected to grow at a CAGR of 38.4% between 2022-2023.
The demand for Artificial Intelligence took off during the 4th quarter of 2022, and it has continued into 2023, and it is likely to continue over the next few years as more startups are launched, venture capital is allocated, and use cases become more and more industry-wide, for each industry.
Increasing investment in AI and machine learning technologies, increasing demand for automated processes, and the growing popularity of AI-as-a-service are major drivers of this growth. Several other factors will also contribute to market growth in 2023, including improved AI and ML hardware, increased cloud computing capacity, and more open-source tools and initiatives, which we have seen firsthand in 2022 with the introduction of the public beta for Stable Diffusion text-to-image, image generation.
👾 Artificial Intelligence Companies To Watch In 2023
Many of the key AI and ML companies are currently private, but companies such as Apple, Google, IBM, and Microsoft are already using AI to solve a wide scope of pain points, both customer and business. In this section, we will review some of the companies in the space that are most likely to see massive growth in the coming years and decades.
⚛️ Stability AI: is an open-source platform that makes use of AI and machine learning technology to automate and enhance business operations. With a diverse community of over 20,000 developers and AI experts, Stability AI provides a range of services, such as an audio production tool, the Stable Diffusion model and the Dream Studio interface for text-to-image generation, and OPenBioML for biology research.
Their aim is to make AI and ML research democratic, to keep technology open source and accessible to all, due to the mounting concern that one group, organization, or government would have the greatest influence over AI IP.
⚛️ OpenAI: Open AI is a research laboratory and another important part of the AI research landscape, backed by Elon Musk and other knowledgeable technologists. OpenAI is devoted to creating artificial general intelligence (AGI) with the purpose of advancing digital intelligence in a way that is most advantageous to humanity overall, irrespective of the domain.
Among OpenAI’s activities are general algorithms, language generation, and robotics, which are some of the anticipated key areas for development with the recent launch of Chat GPT-3 . It is capable of producing human-like writing like never seen before.
Apart from NLP, OpenAi has also introduced a text-to-image generative AI model called Dall-e, Whisper a free neural network, and many more general-purpose solutions.
⚛️ DeepMind: DeepMind is a research institution that focuses on machine learning and artificial intelligence. It was established in 2010 as a subsidiary of Google Alphabet inc. Its mission is to use intelligence to push forward science and benefit humanity.
DeepMind’s long-term goal is to design and build artificial general intelligence (AGI) systems that can solve a variety of different problems. To test the AI’s capability to take action on its own, many of the early models were trained with popular video games. These programs learned how to play a total of 49 Atari games from scratch by analyzing the pixels and scores on the screen.
This led to the development of AlphaGo and AlphaZero, two programs that the company is best known for. The programs defeated some of the best Go players in the world. Go is an ancient Chinese board game that has been used to teach strategy and probability for centuries. By designing algorithms and techniques, DeepMind enables machines to think and learn more like humans. The computer learned to play the game without any human interference, all it referenced was the principles of the game, and was trained on thousands of previous games.
In addition to healthcare and energy efficiency, DeepMind’s technology has also been applied in a variety of fields and is expected to be applied more widely in the years to come, with major planned updates in 2023.
⚛️ TensorFlow: Released in 2015, TensorFlow is an open-source library for machine learning and artificial intelligence developed by the Google Brain team and licensed under apache. It is designed for deep neural network model training, but it can also be used for other tasks.
Engineers and researchers can build on top of TensorFlow using JavaScript, Java, Python, and C++, making it a platform with vast use cases.
TensorFlow has been employed by Google in many areas such as RankBrain, to improve the search results of the Google search engine, and by the healthcare sector with their DermAssist program to identify skin cancer. Additionally, it has been used in eCommerce to provide more accurate and personalized search results. These are just a few of the numerous applications of TensorFlow and its impact on the history of web 2.0.
⚛️ Microsoft: Microsoft is often not recognized as a leader in the AI space, yet just like Apple, Google, and others in the web2 world, it is driving the industry forward at a rapid pace. In Q4 2023, Microsoft stated a hefty $20 billion investment for its data science and AI departments, in addition to a multi-year, multi-billion dollar investment in OpenAI after ChatGPT shook the world in 2022.
Throughout its entire portfolio, the company has used AI and ML in various ways, ranging from Bing search optimization to Cortana, Microsoft’s virtual assistant that was previously available through Xbox. AI and machine learning are also heavily used by Microsoft’s cybersecurity and fraud detection teams to detect and respond autonomously to threats.
Additionally, Microsoft has released hardware-based sensors and computational photography over the last few years that leverage AI and ML heavily. The Skype protocol, Skype’s proprietary Internet telephony (VoIP) network, also uses machine learning to learn the difference between clean speech and noise to optimize bi-directional audio quality and noise cancellation.
In the tech space, if you think of a core product, AI and machine learning are most likely being used under the hood to enhance the user experience.
⚛️ Apple: Apple is not typically seen as a leader in the AI field, but they have been making strides in this area for its consumer products that support the computing needs of the AI-driven future. Their neural engine and Apple silicon are engineered to run applications that require the local device to manage data processes. This includes tasks such as image recognition, optimization, voice processing, speech recognition, and Siri. Each of these is powered by AI to ensure the optimal experience for the user.
I anticipate that Siri will take a giant leap forward in their voice assistant service in the coming years, prompted by the proliferation of ChatGPT and other language models. This will require OEMs to rethink their AI voice assistant strategies so as to include a more comprehensive offering.
AI and ML are also employed in Apple products to improve the user experience, going beyond the areas already mentioned. From video optimization to the noise-canceling function of the Apple AirPods, AI technology is present in numerous Apple products.
💥 📊 Areas of AI Expected To Grow In 2023
1️⃣ Natural Language Processing(NLP): NLP is the study of how computers interact with human language. Using large amounts of text and spoken data, patterns, trends, and relationships are identified. A wide range of applications uses NLP, from automated customer service to text generation and summarization.
2️⃣ Generative Adversarial Network(GAN): GANs generate synthetic data that is indistinguishable from real data by competing between a generator and a discriminator. In addition to improving the performance of machine learning models, GANs have been used to generate realistic images, videos, and audio, and are currently being employed for many of the generative AI consumer products that have taken the world by storm over the past few quarters, starting in 2022.
3️⃣ Automated robotic processes(RPA): RPA uses machines to perform tasks normally performed by humans. Automation of repetitive tasks and processes, such as data entry and customer service, is one of its applications. The benefits of RPA include reduced costs, improved accuracy and efficiency, and a better customer experience.
4️⃣ Deep Reinforcement Learning(DRL): DRLs are a subfield of machine learning inspired by how the brain learns. Using a DRL model, agents interact with simulated environments to learn how to accomplish a goal. Reward signals are generated by the environment, rather than by a teacher. Through trial and error, it effectively learns. With DRL algorithms, massive data sets can be analyzed to produce hyper-precise, or accurate, objectives/outcomes.
Video games, NLP processes, computer vision, education, transportation, finance, healthcare, and robotics are some of the use cases for DRL.
5️⃣ Neuromorphic Computing: Is an approach to computing based on the structure and function of the human brain. Any device that uses silicon to construct artificial neurons is called a neuromorphic system or chip – Neuromorphic computing is used in artificial intelligence, vision systems, head-eye systems, auditory processors, and robotics.
6️⃣ Neural machine translation(NMT): Using NMT, a set of procedures are used to predict the likelihood of a set of characters in a sequence. Text snippets, words, or entire documents can be included. In NMT, deep neural networks and artificial intelligence are used to train neural models for language translation. SMT (statistical machine translation) has been replaced by NMT (natural machine translation) in recent years because the latter provides better translation results.
However, many researchers argue that SMTs will remain relevant in certain cases because they provide more accurate outcomes. Consequently, companies are using hybrid machine translation techniques that combine SMT and NMT. You can read more about this hybrid approach in this article from Omniscien.com.
7️⃣ Deep Neural Network(DNNs): DNNs are a subset of machine learning and artificial intelligence and are based on algorithms that mimic the structure and processes of the brain to give computers intelligence. In the context of machine learning, there are use cases where the technology performs poorly. As an example, when dealing with unstructured data such as images, audio, and other types of unstructured data.
Until relatively recently, neural networks were limited by the amount of computing power they required, but improvements in how computers to process massive data sets have made them more affordable to train.
🌎🎖️Conclusion
With that, we’ve come to the end of our look at the AI and generative AI business trends for 2023. We hope you’ve gained a better understanding of the potential of these technologies and how organizations can leverage them to drive success. Thank you for joining us, and we look forward to seeing the amazing things AI can do in the coming years!
As a quick recap of what we reviewed in this post…
We started off by going over the generative AI application landscape. Next, we reviewed a brief analysis of market data for AI and ML between 2022 - 2023. We concluded with a review of the Artificial intelligence companies to watch in 2023, and the areas, technologies and models that are expected to grow in 2023.
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