AI for the masses
Consumerization is happening now with AI as it becomes more accessible to people. Perhaps its role in ordinary people’s daily lives is more critical to AI’s long-term survival than its influence on business process optimization. Perhaps the most crucial step towards achieving this aim is mainstreaming AI in today’s culture. The concept was first created on the cloud, and as cloud computing became more generally available, the cloud became a standard feature. As a result, expenses may be reduced and passed on to customers. AI, or artificial Intelligence, has experienced increased commoditization in recent years.
What Is AI Consumerization
In recent years, there has been a tendency described as the “Consumerization of IT,” in which users have dissolved the barriers between their work and personal life by utilizing the same mobile devices for both. This has ensured the long-term success of mobile technology. Mobile devices are becoming increasingly popular in both contexts due to enhanced connectivity and ease of access. The consumerization of Artificial Intelligence is having the same impact today. The impact of AI on regular consumers’ lives may be more crucial to its long-term success than its impact on corporate process optimization. The development of AI applications in the business sectors may not be doing as much to increase AI’s presence in society as the consumerization of AI. This accomplishment may be mainly done through integrating AI into modern culture. However, to effectively raise adoption rates, its presence in these places must combine visibility with commercial value.
Digital assistants may be the most common method for clients to connect with AI regularly. Virtual assistants are becoming increasingly common across all platforms, including desktop computers, smart speakers, and home appliances such as pizza delivery services. Most of them have speech recognition features that make it easier for NLP to perform various tasks. Due to machine learning and deep learning breakthroughs, these digital “robots” are assigned more complex tasks. Digital assistants may anticipate client wants, unlike chatbots, usually used to arrange customer service. These assistants may alert users of upcoming events, provide other routes to avoid traffic, and even promote things depending on the user’s preferences.
Deep Learning which also incorporates neural network algorithms designed to mirror the human brain closely, uses a wide range of data to attain this capability. Depending on the use case, autonomous cars, and virtual assistants can process data about the time, weather, the user’s position, and personal information.
What Is AI Commoditization
The influence of AI on our daily lives is already significant and is predicted to rise. AI has become commoditized because of significant breakthroughs in machine learning. Cloud computing and advanced algorithms have made accessing enormous computational resources and data stacks easier.
Natural Language: Consider one of the two most important uses of natural language in delivering the advantages of AI to people. Having a machine comprehend you when you speak standard English is a significant step toward natural language understanding. That was not conceivable seven years ago unless you were at the very top or at a higher learning institution, such as a university, where they had access to the essential data. It is no accident that these things are happening at the same time. AI has become a commodity.
Computer Vision: Another example of AI’s commodity nature is the ubiquitous usage of computer vision software. Deep Learning, an AI technology that can recognize objects in images, has seen broad acceptance due to AI commoditization. It might be an algorithm that analyses artworks, determines the artist’s favored approach, and then recreates the piece in that manner (you may end up with a Van Gogh that was not painted by Van Gogh!).
Data and Algorithms: More individuals will have access to AI as data sets rise and AI algorithms improve. Because major firms have the resources to research and apply AI, they will have no edge over smaller competitors who do not. This is because strong opponents will have the same resources as famous players.
The availability of cloud services has significantly influenced the commoditization of AI. It enables users to share AI with the rest of the world. Most businesses no longer require a big stack or massive processing capacity since they can easily outsource the processing effort to companies like Amazon, Google, or Microsoft. Moreover, because the data is kept in the “cloud,” it is easy to retrieve. Furthermore, algorithmic advancements aid in the reduction of the size of consumer-facing software.
More data implies more Intelligence for the system, and AI is already quite intelligent. Previously, the phrase “Artificial Intelligence” was only known to academics and a limited number of customers. However, because AI has been commoditized, we can now employ AI services geared toward consumers from the comfort of our homes. Several sorts of AI-powered help are becoming increasingly common. Consumers are becoming more acclimated to using AI capabilities as they become more generally available.
Developments in AI in the context of Consumerization and Commoditization
One of the last development leading to AI’s consumerization is the ability to make advantage of a microprocessor or hardware specialization. Conventional central processing units (CPUs) are being replaced by specialized microprocessors designed to execute complex machine learning and deep learning algorithms.
This includes the following:
- Graphics Processing Units (GPU): A specialized electronic circuit designed to interface with a central processing unit and generate 2D and 3D visuals. It is also known as a graphics card in the gaming community. The usage of graphics processing units (GPUs) is increasing across various computer workloads, from financial modeling to state-of-the-art and beyond.
- Tensor Processing Unit (TPU): customized hardware for Google’s open-source machine learning framework TensorFlow. TPU is designed to handle typical training and inference tasks in machine learning and neural networks, such as matrix multiplication, dot product, and quantization transformations. Based on Google’s claims, the TPU is 15–30 times quicker than traditional GPUs and CPUs when inferencing neural networks for usage in AI.
One development in AI in the context of commoditization is the development of the smartphone in 2002 marked the emergence of the most significant and rapid development. Smartphones have advanced significantly; each year, Apple, Samsung, and other Chinese companies introduce new models with significant advances.
The recent development in the context of commoditization is ChatGPT. The area has progressed from clever computers to intelligent robotics to artificial intelligence-based software. A thermoregulator is a simple mechanical device that uses essential but helpful Intelligence to keep the temperature at a predetermined level.
ChatGPT is an Essential Step in AI Commoditization
ChatGPT is an essential step in AI commoditization as this most current generation of generative AI systems was created utilizing foundation models, which are massive, deep learning models that were trained on huge, varied, unstructured data sets (such as text and images) that cover a wide range of topics. With little fine-tuning required for each action, developers may adapt the models to various application scenarios. For example, researchers utilized an earlier version of GPT to create new sequence data, and GPT-3.5, the model that supports ChatGPT, has also been used to translate text. In this method, everyone may profit from the power of these capabilities, even engineers without specialized machine learning skills and, in some instances, non-technical users. Furthermore, adopting foundation models can significantly reduce the time required to construct new AI applications.
Opportunities For New Business Models
To support these consumer-driven volumes, new levels of efficiency and scale are required, which is changing many traditional data center models and processes. The dependence on low-cost commodity servers, N+1 system redundancy, and mainly autonomous data center operations are among the significant opportunities for new models. The advancements made in the technology that supports fields such as algorithms, artificial Intelligence, machine and deep Learning, and big data are as significant. It would appear that the consumerization and commoditization of AI have the potential to transform a substantial part of the entire computing stack, ranging from individual devices to a number of the most challenging large-scale problems. This concept is based on the idea that AI can learn.
Artificial Intelligence is becoming a commodity as more user-friendly technologies become more readily available. Its increasing accessibility and availability is an open invitation to build a new world of faster, better and on-point services and applications. It is a threat to businesses relying on more traditional ways of building and offering value and there are many aspects which still need to be solved, like the intellectual rights of the content created or recreated by AI. However, there is no stoppage in the revolution.