The state of artificial intelligence technology has changed dramatically in the last decade.Instead of just taking over simple tasks, AI is making more and more things possible. For example, DataHunt's parent company, FiscalNote, uses AI and big data technology to collect and analyze information on policies, laws, and regulations from governments, parliaments, and courts, serving more than 5,000companies, public institutions, law firms, and non-governmental organizations(NGOs) around the world. Providing such a vast amount of data in a contextualized manner was unimaginable even 10 years ago, and even more will be possible in 2023. Here are five AI trend keywords that companies dealing withAI and big data must pay attention to.
There are many reasons why domestic companies, especially SMEs and startups, are unable to actively promote the adoption of AI. Experts have cited the lack of specialized manpower and technical skills as the biggest reasons, which is why many companies were stuck at the threshold of AI growth in 2023.
At DataHunt, we ultimately envision a future where everyone uses AI, whether they're a developer or someone in a data blind spot. In 2023, AI industry experts predicted the eventual democratization of AI, explaining that "by enabling anyone to become a data scientist or engineer, the power and utility of AI will be accessible to everyone." We believe that AI will reach its full potential when it is available to everyone and every organization can benefit.
The hottest AI in2022 is the image, video, and speech generative AI model. The popularity of synthetic data also generated a lot of buzz, as high-quality synthetic data could be used to train machine learning to learn so much more, and it didn't have to be bound by norms or restrictions, giving us much more freedom to use data that was previously restricted.
In 2023, experts predicted that companies would use content creation AI like this to generate synthetic data for multiple uses. With synthetic audio or video data, there would be no need to capture movies and speeches from actual video; they could simply input what they wanted their audience to see and hear into a generation tool and the AI model would create it for them.
However, synthetic data still has a lot of challenges to overcome: it hasn't completely overcome the negative perception of so-called "deep fakes." The notoriety of deep fakes is not negligible, as it only emphasizes the benefits of reconstructing the face of a retired actor so that fans can feel nostalgic by seeing him or her in action. Image-generating AI models have also been unable to sidestep copyright controversies, as their training data were paintings by existing artists. In other words, the market for generative models will need to address the above controversies in a healthy way if it wants to continue its current positive trend.
In 2023, more people will be working with robots or smart machines specifically designed to help them perform tasks efficiently. We're already seeing them in retail and industrial workplaces, and they often take the form of augmented reality (AR)headsets that provide instant access to data analytics. They're being hailed as examples of how technology can maximize productivity, which means that IronMan's faithful assistant, Jarvis, may not be far off.
Virtual assistants powered by artificial intelligence can not only respond to questions quickly, but can also offer more efficient alternatives in business decision-making. Asa result, the ability to work with smart machines will become increasingly essential and commonplace, the experts said, adding that the growth gap between companies that have such AI and those that don't is bound to widen.
However, the intersection of AI and augmented reality hasn't necessarily been a success. In2014, Amazon launched its own homegrown smartphone, the Fire Phone. It's considered one of Amazon's biggest failures, and some say it was way ahead of its time. The Fire Phone featured four infrared cameras that allowed computer vision algorithms to be processed in real time. Just like the iPhone X'sTrueDepth camera recognizes faces and a neural engine processes the data.
The cameras were then utilized in a search feature called Firefly. Firefly was a flop, but the system was ported over to Amazon GO, which ultimately pioneered a mechanism for checkout that analyzed moving carts without the customer ever placing an item on the counter.
For all the talk about the rosy future that AI innovations bring, there are still many who say that the vision is overblown: it's still far from perfect in terms of reliability. The most useful and powerful use cases for AI are those that deal with highly sensitive data, such as health or financial information. No matter how accurate a predictive analytics model is, if it doesn't earn the trust of its users, its utility drops dramatically. If the general public doesn't trust the AI itself or understand how it makes decisions, they won't hand over their information.
This means that those working on AI projects need to be able to explain to many people how the model came to make the decisions it did, and be transparent about what information was used. AI's own judgment is not perfect - it's likely to introduce biased data or automated bias as it learns from existing data. What happens when AI has access to areas that require sharp judgment, such as employment, justice, or healthcare? Experts have pointed out that for the unconditional success ofAI projects, we need to stop deceiving users and consumers, because it's no different than a glossy dog and pony show if it's only presenting a good face while burying the system's biases and injustices.
This is why we don't leave the judgment of our artificial intelligence models entirely in the hands of the data we process and handle. We want to make sure that the platform we've built is reliable, but we also want to reduce the "what if" errors that can occur along the way. Better technology can only get better with time.But the slightest misjudgment can lead to the wrong decision, which is whyDataHunt still insists on a human-in-the-loop approach that requires a human touch.
Every industry is increasingly concerned about the environment. The AI industry should also strive to reduce its carbon footprint and minimize its environmental impact. AI algorithms in particular require large amounts of power and resources, such as cloud networks and edge devices, so it's time to start talking about sustainable AI.
The industry must work to build an infrastructure of renewable energy for sustainable AI, not just develop it for the sake of increasing corporate profits, but as a global community to solve the pressing problems facing our planet. Experts are highlighting sustainable AI as an issue to keep in mind for successful AI projects in 2023. They pointed to the need for a plan based on creating products and services in an energy-efficient way, using renewable energy and more.