Outlook 2023: The digital future lies in removing the shackles of artificial intelligence

Artificial intelligence is finally proving that the hype that has been surrounding it for decades is correct. Although artificial intelligence is not yet the savior of mankind, it has advanced from concept to reality, and its practical applications are making our world a better place.

However, many of the miraculous feats of artificial intelligence are hidden, and its impact can only be observed when you look past the mundane guise. Take, for example, a large insurance company operating in more than 30 countries. The company handles more than 20 million customer calls each year. By leveraging speech-to-text technology and natural language processing, they are able to analyze the content of calls to meet specific business needs: control sales quality, understand customer expressions and needs, obtain emotional feedback and analyze data, and more.

Let’s take a look at AES, the world’s top renewable energy producer. Renewable energy requires more equipment to manage and monitor than traditional energy. Data science and AI improve AES’ operational efficiency through automation and provide data-driven insights that enhance performance engineers’ actions and decisions. This ensures that uptime requirements are met and clean energy is delivered to customers as quickly, efficiently and cost-effectively as possible. AES is also doing its part to save the world.

These, like countless artificial intelligence applications that have been put into production, are gaining more and more attention. So far, however, the potential of artificial intelligence has been limited by three key limitations:

• Insufficient computing power;
• The need to bind data to Specific (concentrated) location;
• lack of training data.

Thanks to a few key technological innovations, a sea change is taking place, AI is breaking free of these constraints, and businesses must be ready to take advantage of this powerful technology.

Let’s take a look at these constraints—the shackles that hinder the development of artificial intelligence—and how they can be broken down in the future.

AI Shackles 1: Computing Power

Traditionally, enterprises have not had enough processing power to drive AI models and keep them running properly. Enterprises have been considering whether they should rely entirely on cloud environments for the resources they need, or whether it would be better to allocate computing investments between cloud and on-premises resources.

In-house, on-premise GPU clusters now offer enterprises an option. Today, several larger, more advanced organizations are focusing on production use cases and investing in their own GPU clusters (e.g., NVIDIA DGX SuperPOD). GPU clusters give enterprises the dedicated horsepower they need to run.

A large number of training models - if they utilize a software-based distributed computing framework. Such a framework can abstract away the difficulty of manually parsing training workloads on different GPU nodes.

AI Shackles 2: Centralized Data

Data is typically collected, processed, and stored in a centralized location, often called a data warehouse, for the company's work creation A single source of truth.

Maintaining a single data repository makes it easy to manage, monitor, and iterate. Just as companies now have the option to invest in online or cloud computing capabilities, there has been a movement in recent years to create data warehouse flexibility by decentralizing data.

Data localization rules may prevent data from distributed enterprises from being aggregated. And the rapid emergence of edge use cases for data models makes the concept of a single data warehouse no longer an absolute.

Today, most organizations are running hybrid clouds, so gone are the days when data needed to be tied to a specific location. As we see enterprises continue to leverage hybrid cloud, they gain all the benefits of hybrid cloud—including the flexibility to deploy models at the edge.

AI Shackles 3: Training Data

The lack of useful data has always been a major obstacle to the proliferation of artificial intelligence. While we are technically surrounded by data, collecting and storing data can be time-consuming, tedious, and expensive. There is also the issue of bias. When AI models are developed and deployed, they need to be balanced and free of bias to ensure that the insights they generate are valuable and do no harm. But just as the real world is biased, so is data. To scale the use of your models, you need large amounts of data and an effort to correct for data bias.

To overcome these challenges, enterprises are turning to synthetic data. In fact, synthetic data is on the rise. Gartner estimates that by 2024, 60% of data in AI applications will be synthetic. For a data scientist, the nature of the data (real or synthetic) is irrelevant. What matters is the quality of the data. Synthetic data eliminates potential bias. It is also easily scalable and cheaper to purchase. Synthetic data also gives companies the option of pre-labeled data, significantly reducing the time and resources required to produce and generate the feedstock used to train models.

The Rise of Artificial Intelligence

As artificial intelligence is liberated from the constraints of data quality, computation and location, more use cases involving our daily lives and more precision The model will appear. Already seeing leading organizations using AI to optimize business processes, those that don’t take action to keep up will be at a distinct competitive disadvantage.

To reap the full benefits of AI, implementation needs to start from the top down. While data scientists do the hard work of model development and deployment, executives must also be educated on the concepts in order to best integrate AI into their business strategies. Executive leaders who understand AI technology and its potential can make better strategic investments in AI and, therefore, their businesses.

Conversely, when they don’t know how effectively AI can support business goals, they may just put money into certain applications and hope that new research projects leveraging AI and AI will bear fruit. This is a sub-optimal bottom-up approach. Instead, executives need to work with data science practitioners and employee leaders to learn how to best integrate these technologies into their regular business plans.

In 2023, we can expect to see the stranglehold of artificial intelligence gradually loosened (if not completely broken), so it is time for enterprises to help unlock the full potential of artificial intelligence by investing in solutions , these solutions will make the world a better place, helping these businesses stay competitive in today's digital economy.

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