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10 Predictions For AI In 2023

It’s that time of year again, when artificial intelligence (AI) leaders, consultancies and AI tool vendors look at enterprise trends and make their predictions. After a very challenging, unpredictable 2022, it’s no easy task this time around. Having gathered insights on business challenges and priorities for 2023 from many corporate leaders, I believe these are top 10 AI and ML predictions that company leaders should look into;

1. AI will completely transform security, minimising fraud events

AI and powerful data capabilities redefine the security models and capabilities for small and large organisations. Security practitioners and the industry as a whole will have much better tools and much faster information at their disposal as AI-powered tools are able to spot unusual patterns much faster than any human would ever do. This way, companies should be able to isolate security risks with much greater precision to prevent similar events happening in the future. AI tools will also allow to better understand anomalous behaviour and bad actions by translating data into insights. In the near future, we may very well see parties using AI to infiltrate systems, attempt to take over software assets through ransomware and take advantage of the unregulated cryptocurrency markets.

2. AI will drive the future of customer experience

It’s been a while since organisations have been prioritising digital engagement, understanding that every interaction and its learnings count. While the emergence of RPA automation and the likes initially resolved basic queries, current capabilities of AI are now providing more advanced capabilities: personalising interactions based on customer intent, empowering people to take action and self-serve and making predictions on their next best action. The only way for businesses to scale a quality digital experience for everyone is with an AI-driven automation solution.

AI adoption will become a C-level priority for brands, both B2B and B2C, in 2023, as they determine how to evolve from a primarily live agent-based interaction model to assistive one (human in the loop) that can be primarily serviced through automated interactions, sensing when it’s time pass query to a human to complete a resolution. AI will become necessary to scale operations and properly understand and respond to what customers are saying and what are their true intentions, so brands can learn what their customers need and plan accordingly.

3. Deep learning opportunities will boost demand for GPUs

The biggest source of improvement in AI has been the deployment of deep learning — with special focus on transformer models in training systems. In a nutshell, those are meant to mimic the action of a brain’s neurons and the tasks of humans. The challenging fact is that these breakthroughs require tremendous compute power to analyse vast structured and unstructured datasets. Unlike CPUs, Graphics Processing Units (GPUs) can support parallel processing that deep learning workloads require. That means in 2023, as more applications built around utilising deep learning emerge, the demand for GPUs will continue to increase.

4. Companies will turn to a hybrid approach to NLP

It is possible that companies may turn to a hybrid approach to natural language processing (NLP). A hybrid approach involves using a combination of different technologies to solve a problem while leaving a smaller carbon footprint. This can be useful in NLP, because different techniques may be better suited for different types of tasks or languages. For example, a company may use a rule-based approach for tasks that require a high level of precision, such as translation, and a machine learning-based approach for tasks that require a high level of generalisation, such as text classification. A hybrid approach has shown to produce explainable, scalable and more accurate results, therefore, investments in AI-based natural language technologies will grow. These solutions will have to be accurate, efficient, environmentally sustainable, explainable and not subject to bias. This will require enterprises to abandon the single-technique approach such as just machine learning (ML) or deep learning (DL) for their intrinsic limitations.

5. The role of AI and ML engineers will become mainstream

Since model deployment, scaling AI across the enterprise, reducing time to insight and reducing time to value will become the key success criteria, AI/ML engineers will become critical in meeting these criteria, likely taking over the pedestal from Data Scientists. Today a lot of AI projects fail because they are not built to scale or  integrate with business workflows.

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6. ChatGPT will transform contact centres, but not the way you think

ChatGPT has gone viral since OpenAI released the text-based artificial intelligence tool two months ago. Chatbots are the obvious application for this technology, but they are probably not going to be built first. Today, ChatGPT can answer questions, but it cannot take actions. When a user contacts a customer service of a brand, they sometimes just want answers, but often they want something done — cancel an account, process a return or transfer funds. Secondly, ChatGPT can answer questions based on knowledge available on the internet, and it is only trained up to generic data available before 2022. It doesn’t have access to other knowledge such as internal tribal knowledge. Finally, ChatGPT excels at text generation derived from existing information found online. Yet, when a user contacts a customer centre, they often don’t want creative output — they want immediate actions. All of these issues will get addressed, but it does mean that the first use case is probably not chatbots, but rather a place to learn the first layer of generic knowledge (e.g. instead of directing questions to Google).

7. AI will be at the core of connected systems

In 2023, we’re going to see more organisations start to move away from deploying fragmented RPA, AI and ML applications that replicate human actions for highly specific purposes and begin building more connected ecosystems with AI at their core. This will enable organisations to take data from throughout the enterprise to strengthen ML models across applications, effectively creating learning systems that continually improve outcomes and create incremental outcomes. No longer large corporations will be opting to purchase out of box full solutions, but rather, they will choose modular platforms, which, just like Untrite Intelligence™ , provide building blocks which you pick as you need. The more modules and areas of your business are connected, the more synergy effects can be achieved. For complex organisations to be successful, they need to think about AI as a business multiplier, rather than simply an optimiser.

8. Explainable AI will create more trust

People recognise the power of AI can lead to its misuse, spurring anxiety about how businesses and employers will use AI and Machine Learning technology.  Therefore, over the next year it will become more important than ever for companies to provide transparency into how their AI is applied to personal worker’s data such as the financial one. There are a few reasons why explainability can help create more trustworthy AI. First, explainability can help to increase transparency, which is important for building trust. When people understand how a system works and what factors it takes into account when making decisions, they are more likely to trust it. Explainability can help to identify and mitigate any biases that may be present in the AI system. As the trend continues to grow, more providers will start to disclose how their Machine Learning models lead to their outputs (e.g. recommendations) and predictions, and we’ll see this expand even further to the individual user level with explainability built right into the application being used.

9. Object Recognition + NLP will take search to the next level

While most people write scrapers today to get data off of websites, the more recent developments around Natural Language Processing (NLP) start allowing non-technical users to describe in natural language what they want to extract from a given web page, and the machine pulls it for you. For example, you could say, “Search this trading platform for companies from Switzerland offering automotive bearings, and put all of them in a spreadsheet, along with market cap, employes number etc.”

10. Advances are coming in real-time speech translation

With remote work and high quality access to the internet available in more remote areas, boundaries are becoming increasingly blurred. Today it’s common for people to work and converse with colleagues across borders, even if they don’t speak the same language. Until now, manual translation has been a hiccup that slowed down productivity and innovation. The advancement in AI starts allowing use of communication tools such as Zoom to e.g. speak someone in Japan in their native language while their England is hearing what they’re saying, in English. There have been of course many attempts at real-time speech translation, but only now we’ve reached conditions allowing for scalable and reliable use of such technology, giving businesses more of an opportunity to operate globally.

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