Come 2023, much more progress in the area of artificial intelligence (AI) will be obvious. In 2022, a lot of important milestones were reached, from big steps forward in natural language processing and computer vision to more businesses using AI. The most recent is ChatGPT, a new AI chatbot that took the internet by storm and got Google’s CEO, Sundar Pichai, and his entire team worried.
While preparing this article, Innovation Village did a wide research from studying the evolution of AI – how it grew into a formidable tool and attracted the attention of tech companies all over the world and is seen as the next major technological revolution following the growth of mobile and cloud platforms – to how we use artificial intelligence technology in our daily lives and looked at what experts say will change as we see improvements in the field of AI.
Many of the things we learnt were written or presented in general knowledge. What we have done in this article is to separate the wheat from the chaff, and give you five shifts to expect in the field of artificial intelligence and how to position your business for this new trend in the area of technology.
If you’re new to artificial intelligence (AI) it might be helpful to read this article first before you continue. It covers some basic ideas about AI you should know.
Five changes businesses should expect in 2023
- AI will shape the future of customer service
Do you know the benefits of using AI in customer service are huge? First of all, it’s about efficiency and simplicity. No paperwork, no annoying call-back, no need to wait. Second — AI provides a more personal interaction which is essential when you have a serious customer support problem like recovering a lost password or canceling something. “Servion Global Solutions predicts AI will power 95% of all customer interactions by 2025, including live telephone and online conversations”
The day when AI is helping people by making their daily routine easier has come, and it doesn’t matter if you are a business owner or a customer. AI can now replace your receptionists, order your pizza and deliver it to you, and even check the weather for you and warn you if you need to bring an umbrella on your way.
- There will soon be markets for AI-powered model products
The AI Marketplace is a platform that fosters exchange between users and providers of AI. It is a digital platform that brings together AI experts, solution providers and manufacturing companies.
At the heart of AI are data. And data are as sensitive as they are valuable. Therefore, we need a secure, certified standard that ensures data sovereignty.
Transparency Market Research which delivers key insights on the global artificial intelligence market, says, “The market for AI services is estimated to exceed 5.5 trillion dollars by 2027.”
In an exclusive interview, Bryan Harris, Executive Vice President and Chief Technology Officer, SAS, says industry-specific AI model marketplaces that enable businesses to easily consume and integrate AI models without having to create and manage the model lifecycle are the future. “Businesses will simply subscribe to an AI model store. Think of the Apple Music store or Spotify for AI models broken down by industry and data they process.”
He gave an example using the manufacturing and banking sectors. “Manufacturers are using computer vision to identify quality issues and reduce waste; retailers are using machine learning techniques to improve forecasts and save on inventory and product waste costs, and banks are using conversational AI and natural language processing to improve marketing and sales.”
- As we move toward more interdependent ecosystems, AI will play a central role
The essence of an entrepreneurial ecosystem is its people and the culture of trust and collaboration that allows them to interact successfully. In 2023, we’re going to see more organizations start to move away from deploying siloed 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 organizations to take data from throughout the enterprise to strengthen machine learning models across applications, effectively creating learning systems that continually improve outcomes. For enterprises to be successful, they need to think about AI as a business multiplier, rather than simply an optimizer.
- Using generative AI, business software will undergo a radical evolution.
Generative AI models produce text and images: blog posts, program code, poetry, and artwork. The software uses complex machine learning models to predict the next word based on previous word sequences, or the next image based on words describing previous images.
A good example is ChatGPT, which we covered in this article. It has the ability (using generative AI language model) to create original content in response to a user prompt.
Generative AI can take in such content as images, longer text formats, emails, social media content, voice recordings, program code, and structured data. It can output new content, translations, answers to questions, sentiment analysis, summaries, and even videos.
These universal content machines have many potential applications in business, and today marketing applications are among the most common uses of generative AI. In the future, there is potential for generative AI to make an impact in health care and life sciences—to make diagnoses, for example, or find new cures for disease.
- More businesses will start using open-source machine learning tools
OpenML (Open Machine Learning) is an open platform for sharing datasets, algorithms, and experiments – to learn how to learn better, together. The global machine learning (ML) market is expected to grow from $21.17 billion in 2022 to $209.91 billion by 2029.
Machine learning, which is a subset of artificial intelligence, is a data analytics method that teaches computers to learn from algoriths and data and quickly imitate the way that humans learn. AI and machine learning tools are constantly changing. The best ones allow you to take advantage of their scalability and adaptability.
Tensorflow is the premier open-source AI and ML platform. Python purists should consider PyTorch and MLflow. Another strong Python alternative is Numpy. When it comes to neural networks, Keras is unrivalled, while Pandas is ideal for those who want an environment similar to R or Excel. Which option is best depends on your specific requirements for software development.
According to Moses Guttman, CEO, ClearML, “Next year teams that focus on ML operations, management and governance will have to do more with less. Because of this, businesses will adopt more off-the-shelf solutions because they are less expensive to produce, require less research time and can be customized to fit most needs.”
