Artificial intelligence (AI), machine learning, big data, and other smart technology have become indispensable. Many people, whether at work or going about their day, encounter some form of AI. The degree of how deeply AI is integrated into a business, production line, or daily life, in general, depends on the users' needs.
Chatbots are an excellent tool for customer and client interaction. These simple AIs act as a point of contact with a client, offering helpful answers to a determined set of questions. The small box usually appears in the corner of a website asking, “can I help you?” is a type of chatbot.
Some chatbot AI utilize machine learning (ML) which collects data from these interactions to become more accurate and faster. With chatbots handling the brunt of simple requests, the more involved requests are left to staff. For a company, chatbots save time, costs, and productivity.
It should come as no surprise that chatbots are continually implemented into a company’s business. An AI taking the world by storm, is a sophisticated AI and ML chatbot called ChatGPT.
What is ChatGPT?
ChatGPT combines GPT or generated pre-trained transformer and a chatbot. It is based on the ML model called GPT, to generate more realistic and human-like text responses. GPT is, as the name implies, pre-trained with large amounts of text data for a specific job.
GPT was initially designed for typing automation to build complex sentences and paragraphs with human-like language. With its ML algorithms, ChatGPT can answer questions based on prior knowledge and learn from conversations to become more human-like over time. With these capabilities, GPT is often used for numerous functions beyond chatbots.
ChatGPT can generate messages for news articles and other forms of editorial content, social media posts, customer support, marketing tasks, automated translation, weather forecasts, and more. Utilizing past projects, ChatGPT, can author human-like text for a variety of different writing formats, but there are some limitations. The most significant limitation is that ChatGPT, like other types of chatbots, cannot make decisions on its own. Since it is a pure text generation algorithm, it can generate and present suggestions for decisions using text descriptions. The final decision comes down to the users.
However, ChatGPT is a little more advanced in comparison to other natural language processing (NLP) technologies, including rule-based and ML chatbots, due to its pre-training requirement. Rule-based chatbots are held to their defined rules and decision trees. Their answers become predictable and are prone to errors when unexpected input is submitted.
ML chatbots can typically handle far more complex requests, are less error-prone, and learn from experience. However, ML chatbots are more complicated to maintain and implement than rule-based chatbots. Taking both NLP models and ML chatbots’ strengths, ChatGPT is faster and easier to implement and maintain than either existing technology.
ChatGPT, and GPT in general, will continue to evolve as AI advances. Further software developments will improve its NLP technology to achieve more intelligent generated responses. These responses will become more personalized and contextual through collected information via conversation exchanges. Though it will likely be further down the line as current generated text still requires further editing in larger projects beyond simple chatbot answers.
AI's aid outside ChatGPT is immense, with the ever-growing list of tools it provides. Original chip manufacturers (OCMs), original equipment manufacturers (OEMs), contract manufacturers (CMs), and others benefit extremely from AI’s and other ML tools aid. ChatGPT acts as the perfect example of further growth within companies of all industries to utilize AI for necessary but often tedious tasks.
How AIs like ChatGPT Aid Companies
AI and ML can help semiconductor OCMs at every step of their operations, including research, chip design, sales, and marketing. According to a McKinsey and Company report, only 30% of semiconductor OCMs generate value through their AI/ML tools. This 30% have invested in AI/ML through data infrastructure and technology improvements and scaled up their initial case uses. The remaining 70% are still in the AI/ML implementation pilot phase.
Experts believe that AI/ML applications will “dramatically accelerate in the semiconductor industry over the next few years.” As reported by McKinsey and Company, “AI/ML contributes between $5 and $8 billion annually to earnings before interest and taxes.” Impressive, but hardly scratching the surface of possibility. This is only 10% of AI/ML’s full potential within the industry. Over the next 3 to 5 years, the continual implementation of AI/ML tools like ChatGPT could potentially generate $35 to $40 billion in value annually. Over a longer time frame? McKinsey and Company researchers suggest it could reach $85 to $95 billion annually, which will inevitably be passed on to customers.
Manufacturing is expected to accrue the most value from AI/ML implementation, considering capital, operating expenditures, and material costs for chip fabrication. The most significant spending reduction will come from chip design and verification automation. This is exceptional value now as the semiconductor industry grapples with labor shortages. As a result of pandemic lockdowns and a history of decreased manufacturing for some countries, there are no longer enough staff to run these fabs. More semiconductors will be needed with more construction thanks to OCMs globally spreading out from government incentive programs and subsidies.
AI/ML tools can remove the number of staff needed to operate a semiconductor fabrication plant. AI and ML can optimize portfolios, increase efficiency, eliminate defects and out-of-tolerance process steps, and accelerate yield ramp-up while decreasing time-consuming tasks. OCMs can avoid missteps through ML algorithms that identify patterns in component failures and predict likely errors in new designs by breaking down IC designs with AI-based analytics.
Adoption of AI/ML tools to reach such stages will take time. Tools like ChatGPT, which utilize pre-trained transformers, must be implemented strategically with the usual large amounts of data required to sort through. McKinsey and Company’s research details the six most essential enablers for AI at scale. Creating a strategic roadmap involving expertise from talent, connecting the fab through existing technology, establishing data governance, ensuring smooth integration through digital workflows, and encouraging rapid learning for agile delivery are all key to an AI/ML foundation.
Once finished, it will make OCM operations run smoother and everyone along the supply chain as they also implement AI and ML.
Sourcengine Can Start You Off Right
The most significant hindrance to AI and ML adoption is resources. ChatGPT and other AI require computing power to back up. What powers these tools? You guessed it, the very thing these OCMs produce. Semiconductors.
AI adoption has fallen behind over the last several years due to the need for these crucial chips. Company plans to implement AI/ML tools have stagnated during the shortage. With the shortage easing, now would be the best time to restart previous AI/ML adoption plans or begin enabling available technologies.
For AI and machine learning producers, getting your hands on a steady supply of semiconductors is necessary as the world transitions to implement more AI and ML. While the shortage is easing, semiconductors will bounce back sooner than later from digital advances. Sourcengine is a global marketplace with over 3,500 suppliers and 1 billion part listings. The search navigation bar will get you where you need to go, and if you can’t find a particular component on your list, send us an RFQ for our sourcing experts’ help.
To ensure you keep on top of the market, so you never lose an opportunity, Datalynq’s predictive analytics and market intelligence can help you strategize for future shortages and periods of excess.