The earliest concept of a “learning machine” was introduced in 1950 in the philosophical journal “Mind,” authored by scholar and scientist Alan Turing. Five years later, the first AI prototype, the Logic Theorist, was launched to continue the momentum. All this occurred well before digital computing capabilities, silicon chips, the Internet, or screen displays.
Thus, looking back at how much artificial intelligence (AI) has progressed over 77 years since AI began is remarkable. With its ability to make precise business decisions, AI catalyzes transformations across various industries, revolutionizing age-old processes they’ve relied on for years.
Despite its evident potential, some businesses and industry leaders are maintaining a cautious distance from AI, hesitating to embrace its advantages to their operations fully. Is your business actively exploring AI’s benefits, or are you inadvertently creating barriers to its adoption?
AI’s Origins and History
In the summer of 1956, at a seminal event that marked the unofficial start of artificial intelligence, mathematicians and physicists gathered at Dartmouth College to research machine learning work. The group’s stated aim was to understand how to devise a framework of thought to understand human intelligence better and how to make machines more informed with that intelligence.
Over the years, AI progressed steadily, though mainly in research labs. Machine learning models were rudimentary, training data was scarce, and computing power was limited. But the building blocks were there: neural networks, deep learning architectures, and increasing data generation.
Around 2010, things started taking off. The rise of big data, GPUs, and open-source frameworks like TensorFlow fueled rapid advances. Investment poured in, and academics began launching startups.
The last decade was indeed the breakout period for AI entering the mainstream. AI platforms allow organizations to apply AI processes without needing a Ph.D. in machine learning. We saw more commercial applications for various AI actions, like computer vision, NLP (Natural Language Processing), digital assistants, and more.
Generative AI
Today, AI is pervasive in recommending what movies we watch and what routes we drive, and it answers many of our questions. In recent months, AI has moved beyond pattern recognition into generative abilities like creating original text, images, sounds, and more. AI models also continue to get bigger, faster, and more capable, unlocking countless possibilities for how they will impact the future of work.
Generative AI is getting the bulk of media coverage, with excitement and trepidation in equal measures for such tools like text-based ChatGPT, Bard, and others; image-based Dall-E and Midjourney, and other AI-assisted business-finance-operations tools coming to market.
At the same time, the potentially limitless possibilities of such tools have instigated calls for further research and regulation worldwide. Similarly, companies will need to be watchful about the AI strategy to ensure they adopt sustainable tools.
Barriers to Adoption
In some business segments, AI adoption has been limited in scope, thereby stymying its spread and growth. AI’s potential use levels far exceed today’s readiness levels of most modern businesses. There may be a lot of catch-up to accomplish.
The cycles of hype and disillusionment in AI also foster skepticism. Setbacks on the Gartner Hype Cycle have made businesses extra cautious about large-scale AI deployment.
One of the missing links in the equation is having clean, quality datasets. Robust data remains crucial. Indeed, AI’s capabilities have grown over time, yet they are often heavily reliant on being fed quality data.
Unfortunately, most companies have fragmented data spread across siloed systems. Cleansing and consolidating it requires substantial data engineering efforts. And dirty data leads to faulty model outputs no matter how advanced the algorithms are.
AI tools can help. Companies can invest more strongly in data pipelines and governance to improve internal data quality, explore targeted, well-scoped AI proofs of concept for the business, and view AI as an ongoing journey that demands patience and a willingness to learn.
Areas for AI Improvement
For those of you more willing to jump headfirst into AI adoption, here are areas in which AI can make substantial improvements almost immediately:
- Automation of tasks around data entry and analysis
- Natural language processing models that can identify discrepancies in data
- Inventory management and asset maintenance
- Augmenting human workforces to improve productivity and minimize time spent on mundane tasks
- Capturing the institutional knowledge of experienced workers (i.e., have your teams continuously train the models)
Questions about new situations will arise as AI progresses across natural language processing, computer vision, predictive analytics, and more. This is where AI applications like our own Trusted Supply can help. This software incorporates AI natural language models to develop and collaborate on upstream and downstream opportunities between buyers and suppliers, regardless of data quality or the system chosen.
The upcoming decade will witness AI becoming an integral part of our lives, delving deeper into various aspects. As AI models evolve, they will demand less data for training, resulting in highly personalized experiences. Amid this rapid expansion, ethical considerations and governance will take center stage, ensuring responsible AI usage. Undoubtedly, AI is one of our time’s most transformative technologies, promising a future filled with innovation and possibilities.
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