Potential separates the commonplace from the prosperous. This has been – and always will be – a fixed facet of success, regardless of whether it’s based on a solid groundwork of innovation, an intermingling of well-executed strategies, or, yes, a dash of luck.
Artificial intelligence is in no way exempt from this factor. While it’s fair to say that most eyes are on Microsoft, Apple, and Google’s ventures, those looking to expand their own undertakings through the (seemingly) limitless power of AI should re-focus their gaze on the “underdogs” – even if that title could be seen as a misnomer. That’s precisely what we’ll be doing today: taking a small but valuable peek at the lesser-talked-about services with high expectations – and an even higher probability of meeting them.
…but before that, let’s lay down a few points of reference that will help us establish a barometer.
Our Criteria
For starters, growth potential must always be examined through a prism of general movements and subtle yet solidified trends. Funnily enough, due to AI’s marked freshness in the field of software technology, we’ll be relying on that exact prism as a magnifying glass toward the larger tech world as a whole.
What’s more, these services provide us with a glimpse into the extensive system of combined frameworks and innovations that make up AI creativity. This entails questions like “How well will AI integrate with the organization’s goals?” or, if applicable, “How can it enhance our own productivity or the client’s user experience?”
Let’s answer those, shall we?
GitHub Copilot
A definitive answer to repetitive coding tasks and painfully slow prototyping, GitHub Copilot lives up to its name in spades. It essentially acts as an auto-completion tool for coding, and is bolstered by a wide array of sources, making it a valuable tool for streamlining, generating solutions, and reducing wasted time otherwise spent on documentation overviews.
With the eventual release of Copilot X, its latent qualities are expected to shine brighter than ever. If it isn’t already an essential facet in developers’ lives, the GPT-4-backed integration and advanced voice control may just seal the deal.
The full-scale integration of Copilot is forecasted to result in significant productivity gains, which will entail 15 million additional operative developers worldwide by the start of the new decade.
Eightfold
The prospect of optimizing talent recruitment sounds like a mere dream to most business-savvy folk. Luckily, Eightfold’s talent Intelligence Platform turns that dream into a viable reality for many enterprises. Whether it’s the public sector or specific DE&I-focused industries, Eightfold’s wide yet meticulous applicability aids agencies by developing fully personalized AI that evolves to analyze vast quantities of data related to talent backgrounds, potential skillsets, and aptitudes for upskilling and reskilling.
The company and its service have steadily gained traction thanks to an equal blend of obvious and not-so-obvious reasons. While the industry prepares itself for the eventual reshaping of talent management, the very nature of AI implementation can improve the current state of data management to benefit employees’ rights.
AWS AI Services
Amazon’s very own offering in the field of AI is moderately overlooked in favor of the other giants’ proprietary services – Azure AI, Gemini, and OpenAI’s GPT network. This is, perhaps in part, due to Microsoft’s opportunistic partnerships. Regardless, it’s not as if Amazon lacks the capabilities and features, along with its distinct innovation, to scale them.
Variety is the backbone of the Amazon Web Service network, as the options range from applications for scaling foundation models, deep analysis of image and video materials, chatbots and virtual agents, and even code autocompletion apps for developers.
In particular, steps are being taken to integrate Amazon’s Bedrock service to streamline government-delivered information, thus improving readability for cognitive impairment or specific learning disabilities.
Deepgram
Equipped with an extensive network of tools related to text-to-speech, speech-to-text, and advanced audial detection, analysis, and summation – Deepgram offers full-scale consolidation with other APIs and presents a lucrative answer to enhancing customer service interactions through parsing. In practice, this means companies can focus on optimizing their services to detect and utilize the full potential of keywords, tailored speech models, and streamlined company jargon.
A fully realized example is with Tethr, as they utilize Deepgram for sales conversations. The company, whether directly or indirectly, has benefited from the implementation in improving overall user experience. How? Through improved insight speeds and conversational analysis, helping both sides “cut to the chase” as it goes.
MindsDB
With its infant version launched back in 2017, this fully open-source, GitHub-collaborated project quickly rose the ranks, in due part to its steadfast focus on fine-tuning and deploying predictive models for businesses of all shapes, sizes, and structures. Custom pipeline automatization is seamless, and its applicability – limitless. Allowing for time-sensitive execution and live forecasting, MindsDB has proven particularly lucrative for services such as Nixtla’s NeuralForecast and TimeGPT. There’s a good reason why it boasts over two hundred integrations on its GitHub page alone!
Having been named the 2022 Gartner Cool Vendor in early 2023 and with its further expected rise in the next half of the decade thanks to an approximate $16 million Benchmark investment, it’s safe to say the growth trend has shifted into a full-on oath.
Conclusion
The ever-evolving landscape of artificial intelligence reveals a fruitful field ripe with innovation and potential among less heralded organizations. And that potential is what holds the key to aligning neural networks with specific, real-world needs.
From Eightfold redefining talent management to GitHub Copilot enhancing coding efficiency, these platforms give us a glimpse toward a future where predictive models and live forecasting become integral not just to the core concept of efficiency but to the complete democratization of AI benefits as well. Of course, it’s up to us to look out for that elusive potential.