The state of AI in 2022
Global consultancy firm McKinsey & Company have released a report titled ‘The State of AI in 2022’. The research reveals how AI has been used and adopted during the previous five years.
According to the report, AI adoption has more than doubled. The number shows that AI adoption globally is 2.5x higher today than in 2017.
In addition, from 1.9 in 2018 to 3.8 in 2022, the average number of AI capabilities used by enterprises, such as computer vision and natural language generation, has doubled. Robotic process automation and computer vision have continued to be the most often used of these capabilities each year.
Top 5 AI and machine learning trends that can change the world in 2023
As businesses try to win over customers with intelligent experiences provided in real time on smartphones, smart TVs, smart cars—smart everything—AI continues to change our world. Here are the top 5 big AI/ML trends that we will be tracking in the year ahead, along with recommendations for how enterprises can stay ahead of each trend.
1. Generative AI
We all know that in the past 6 months, Chat GPT and other AI apps have emerged as a phenomenon with surprisingly smart applications and features. These are all empowered by generation AI.
So, what is generative AI?
Artificial intelligence (AI) that generates new content, including audio, code, images, texts, simulations, and videos, is referred to as generative. This includes algorithms like ChatGPT. Recent developments in the sector could fundamentally alter how we think about content creation.
Then, what kinds of output can a generative AI model produce?
The results of generative AI models can be identical to content created by humans or they can have an eerie quality. As we've seen, ChatGPT's outputs thus far seem to be superior than those of its predecessors. The outcomes also depend on the model's quality and how well it matches the use case, or input.
However, the results aren't always correct - or appropriate. For instance, ChatGPT appears to struggle with fundamental algebra issues, counting, or even overcoming the sexism and racism that permeate the internet and society at large.
The data used to train the algorithms are combined in carefully calibrated ways to produce generative AI outputs. This is due to the extraordinarily large amount of data that was utilized to train these algorithms. Furthermore, the models typically have random components, which enables them to generate a variety of outputs from a single input request, giving the impression that they are even more realistic.
Success with generative AI applications will differ for every person or organization. Although using AI-generated apps is a formidable instrument with the potential to provide beneficial and advantageous results, it shouldn't be utilized to replace human discretion, originality, or the crucial function a human performs in content creation.
When implementing generative AI applications, it is imperative to keep in mind the significance of attribution, fact-checking, and personalization.
2. Machine Learning Optimization Management (MLOps)
Data changes over time, which makes machine learning models stale. ML models learn patterns in data, but these patterns change as the trends and behaviors change.
Although we cannot stop data from changing, we can maintain our model up to date with the latest trends and modifications. We require an automated pipeline, MLOps, to do this.
Here are a few top MLOps trends and forecasts for 2023 that will undoubtedly become more well-known in the sector.
Data-based MLOps
Identify Drift
Enhancing the value of ML solutions
An increase in the amount of MLOps libraries and packages
Transferring AutoML to AutoMLOps
According to Vivek Verma - Mid Data Scientist (Innovation) from Toyota Connected North America, a new MLOps platform for NLP and NLU might be developed so as to solve business problems in market research and healthcare among others.
3. Data-centric
Throughout many years, we all know that model centric has revealed the problems coming from the poor data. With poor quality data, even on a well-trained model, leads to bad predictions, and in turn bad business outcomes. That also means building a well-trained model on high-quality data leads to better prediction.
When it comes to feature engineering, low-quality data seeps into ML pipelines far too frequently. This is especially true when it comes to fintech, healthcare, retail, and high-tech organizations with significant production ML footprints.
Read more about data-centric blog here
4. Responsible AI
The development of more moral and understandable AI models is essential. But trust and compliance are the most crucial elements. Data, which frequently includes personal information, is what AI needs to learn. This might be incredibly private data, like health or financial information, for many of the most important and effective AI use cases.
To answer all of the questions about trust in AI, responsible AI is becoming one of the most popular trends for 2023. Responsible artificial intelligence (Responsible AI) is a methodology for creating, evaluating, and using AI systems in a morally and ethically responsible manner. Responsible AI can assist in actively guiding these choices toward more advantageous and equitable outcomes, from the goal of the system to how users engage with AI systems. This entails upholding enduring principles like justice, dependability, and transparency and keeping people and their goals at the forefront of system design decisions.
Microsoft has developed a Responsible AI Standard. It's a framework for building AI systems according to six principles: fairness, reliability and safety, privacy and security, inclusiveness, transparency, and accountability. These tenets are the cornerstone of Microsoft's ethical and reliable approach to AI, particularly as intelligent technology is increasingly incorporated into goods and services that people use on a daily basis.
5. AI for everyone
Once AI is widely accessible and everyone can use it to their advantage, it will realize its full potential. This will be simpler than ever in 2023.
Increasingly more applications put AI capabilities at the fingertips of everyone, including individuals, SMEs, and large corporations, regardless of technical proficiency. This might be as simple as programs that allow us to create intricate visualizations and reports with a single mouse click, reducing the amount of typing needed to do searches or send emails.
It is getting simpler to create your own software if one that meets your demands is not currently accessible because no-code and low-code platforms are becoming more widely available. These enable the development, testing, and deployment of AI-powered solutions using straightforward drag-and-drop or wizard-based user interfaces, making them accessible to practically anybody.
The acceleration of AI will make it possible for all organizations to access AI at reasonable prices and full-packaged service. With the belief that “a better future would be created by a better & more responsible AI development”, Pixta AI is the pioneer in providing full-packaged annotation service to make AI accessible to everyone at anytime and anywhere.
Bottom line
AI is having an impact on every part of our daily life, from voice assistants to video recommendations and binary artists, and everything so far. It is crucial to understand how these new tools and technology can be used to our advantage.
Here is our comprehensive list of the most significant AI trends to watch in 2023. Even while some of these things aren't entirely new, they have evolved significantly over the past few years.
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