Potential of Artificial Intelligence (AI)
AI’s comprehensive goals have become very ambitious in recent years. As this new technology exists in the fields of literature and science, it applies to real problems and seeks real solutions–its true capabilities and use cases need to be considered.
You may be disappointed and unable to fulfill that promise but last year was the time to build the foundation for AI. 2021 provided a framework that can be built and modified to make AI more responsible, efficient, and cost-effective. 2022 is the year to learn from past mistakes and build a better world of AI tech.
You’ll find our top 5 predictions below about the future of AI and why we think these changes are imperative to the overall success of AI technology.
Responsible AI Goes from Aspiration to a Foundational Requirement
In 2021, the AI industry had an all-talk and no-walk problem. While you can read dozens of think pieces and thought leadership articles about responsible AI in 2021, the adoption of responsible AI principles was low. According to the Appen 2021 State of AI report, concern for AI ethics was at just 41% for technologists and 33% for business leaders.
In 2022, the stakes get higher, and businesses will begin to recognize that responsible AI leads to better business outcomes. The business leaders will catch up to the technologists in understanding the importance of responsible AI. And, even more importantly, they’ll begin to see how the upfront investment will pay off for their business.
When responsible AI principles are properly implemented, they protect a business’s brand and ensure that the AI project works as expected. Entering 2022, there is also a set of responsible AI principles that have been established and thoroughly reviewed. These include:
- Bias-free data
- Fair treatment of data collectors and label authors
- The need for AI projects to promote social benefits and prevent social harm
The government is not far behind as business leaders catch up with engineers and recognize the importance of responsible AI. Governments are beginning to recognize the potential harm that irresponsible AI can cause. This perception is subject to regulation. As with privacy, if private companies cannot regulate the harm to society, governments will intervene in regulations that force companies to use ethical and responsible AI.
Another bellwether for the implementation of responsible AI comes from Gartner, which projects that by 2023 all personnel hired for AI development will need to demonstrate expertise in responsible AI.
AI life cycle data will be important for AI programs
Recent statistics and trends show that AI programs are mature, and AI is becoming more and more popular. AI drives business operations and shapes product development. According to the Appen 2021 State of AI Report, AI budgets have increased over the past year. This shows that business leaders are aware that they need to invest in AI to ensure success.
One of the key takeaways from 2021 is that businesses, even those with mature AI data science sectors, are struggling with data. What businesses are realizing is the vastness of the amount of data needed for AI model development, training, and retraining. Since so much data is needed for a successful AI lifecycle, many businesses are choosing to partner with external training data providers to deploy and update AI projects at scale.
The fact that most organizations are pairing with external data partners shows the challenge of continuous data sourcing, preparation, evaluation, and production. AI projects need more data and faster than ever before. This can only be achieved with automation of data acquisition and preparation.
The need for this data will shift to 2022 and beyond. Companies still need the same amount of data but new areas are emerging. AI lifecycle data focuses on developing tools and best practices that enable organizations to manage the entire AI lifecycle, from data ingestion to data versioning to model retraining.
Rise of synthetic data
As more data is needed to meet data-intensive AI programs and model retraining, the industry will seek new ways for companies to collect data. External data partners are the only solution to receive more data at the speed needed; however, other solutions are emerging.
Generative AI can create synthetic data that can be used to train AI models. Currently, it accounts for only 1% of the data on the market but Gartner believes that generated AI will account for 10% of all data generated by 2025. Today, generative AI is being used to address key challenges, such as generating 3D worlds for AR/VR and for training autonomous vehicles.
Gartner also forecasts that by 2024, the use of synthetic data will halve the volume of real data needed for machine learning. The use of synthetic data complements and accelerates the data acquisition process because it needs less processing, security, and labeling than real world data which is subject to responsible AI principles.
In 2022, you can expect more businesses and machine learning models using and experimenting with synthetic data. Generative AI models can learn from themselves and generate new data, which is both cost effective and improves efficiency. With these benefits, it’s obvious why many businesses are excited about generative AI and synthetic data. And, as more companies experiment with and implement synthetic data and generative AI, we’ll see new use cases developed over the next few years.
Acceleration of Internal Efficiency Use Cases
Some great news for the industry: AI budgets are on the rise, according to a recent report. 74% of respondents reported that they have AI budgets of over $500k. As well, 67% of business leaders say that their AI projects have “shown meaningful ROI”.
As budgets grow and the variety of use cases expands, it’s not surprising the number one most popular use case, at 62%, is supporting internal operations.
- 55% looking to improve their understanding of corporate data
- 54% looking to improve productivity and efficiency of internal business processes
As companies shift towards using AI and machine learning models to improve internal efficiency, they`ll face an important data challenge. Companies now need to know how data moves through their organization and what happens to that data along the journey. As companies make this realization, they’re going to need to make two moves:
- They will need to focus more attention on deploying platforms that enable them to eliminate data silos and centrally manage data
- They will need to work internally or with partners to develop strategies focused on being able to manage data throughout the entire AI lifecycle. If your organization can take these two steps, your AI initiatives will be more effective and efficient.
Model evaluation and optimization will be mainstream
In the AI technology community, one perception is gradually beginning to resonate: Building an AI machine learning model is not complete with just one. Models should be evaluated, optimized, and retrained on a regular basis. In 2022 and beyond, this perception will become common knowledge. The machine learning model is dynamic and cannot be simply deployed and left to its own devices. Like cars that require regular alignment adjustments, machine learning models can drift over time. This drift can cause machine learning model results to become increasingly inaccurate over time. Machine learning models need to be reviewed and updated based on ongoing output and changes in infrastructure, data sources, and business models.
According to a report, the knowledge that machine learning models need to be reviewed and updated on a regular basis has taken a big leap.
- 87% of companies update their models at least quarterly, up from 80% a year ago
- 57% update their models at least monthly
- 91% of large organizations update their models at least quarterly
Organizations using external data providers most often update their models at least once a month. As more companies adopt machine learning models, they begin to realize that once they’re launched, they can’t just rely on themselves. Companies use machine learning models to implement protocols for drifting and regular tuning. The adoption of AI technology and machine learning models are widespread, but this is only the first step. Today, it’s important for organizations to rely on external data partners and educational sources to learn how to manage and improve the use of AI and machine learning.
As part of the maturity of AI, we see the transition from talking about responsible AI to implementing responsible AI programs. By doing so, the enterprise recognizes the important nature of the data. Recognizing its importance for the success of AI projects, external data partners can be used to collect data for the entire life cycle and make synthetic data more cost-effective and safer to use.