In today’s tech environment, conversations can quickly become polarized; either you are singing the praises of AI or pointing out how little of it is applicable to our daily lives. Just six months ago, we hardly heard of generative AI, but now Bing AI and ChatGPT, among others, are a reality. Should we just accept that algorithms will soon replace us or are we missing something important?
Microsoft and OpenAI recently made the news for their development of transformer neural network technology, trained on web data. This caused some unexpected issues, highlighting how little expertise the biggest companies had on the topic. Theirs was a risk that caused other tech bigwigs to be brought down a peg or two. On the bright side, such an ecosystem gave way to “thin wrappers AI companies” that quickly capitalized on the lack of knowledge in the space. See the Amazon-HuggingFace partnership.
Most organizations recognize the potential of AI in improving their business operations. Here’s where things get tricky, though — the technology evolves at such a breakneck pace that businesses seeking to make the most of AI should be aware of the risks that come with launching into the unknown, so that they don’t end up left behind in the dust.
There has been a lot of misunderstanding in the AI space, so let’s take a few of these misconceptions and explore the true facts behind them.
The effectiveness of AI tools is largely dependent on the data used to train the algorithm. Parameters are not a good enough measurability of this; what matters more is the quality and nature of the training data. For instance, one trained with more code would work better for an AI writing code, and not necessarily one trained with more parameters. This means it is essential to know exactly the kind of data and the quantity that went into a model for an accurate assessment of what the results may be.
In the fast-paced AI market, the companies that will succeed are those that are flexible enough to deploy and customize any models. The viability of a product is not all about fancy UI/UX; customer-specific software solutions need to also have rock solid legal and security protocols, as well as the capacity to work in the long run. This is especially true in an industry that is ripe for consolidation. Careless reliance on a single model can quickly lead to vendor lock-in, effectively preventing anyone else from competing in that space.
Another often repeated misconceived notion is that AI is bound to eventually replacing human labour. In truth, technical advancements may indeed make it possible, but the reason it won’t come to pass is much more simple than that — human work relies on more than just data and process. To be successful, people need to be able to trust each other, rely on complex social interactions for problem-solving, funny quips for morale and unspoken solutions for reconciliation. Thankfully, this does not apply to current AI technology.
In light of the above, it is essential for companies deploying AI solutions to: understand the data used to create the algorithms; make sure the vendor is dependable, able to fine-tune the model, and use industry-specific solutions; and finally, to reinforce the significance of teambuilding and human collaboration as valuable elements of the workflow.
The bright news is that successful AI integration is indeed possible. The key to this is being mindful of the information above and being aware of the nuances in AI technology. Companies that apply these concepts can get the most out of AI while avoiding potential pitfalls.
The San Francisco Executive AI Summit will be a great opportunity for decision makers to get up to date on the latest trends in AI technology. Attendees will have the chance to hear AI experts discuss the most practical strategies to achieve enterprise success, as well as to network with other innovation-minded professionals. Don’t miss out and make sure to join the top executives in San Francisco on July 11-12.
