Top AI Trends to Be on the Lookout for in 2023
It seems like every year AI keeps getting more and more advanced. One of the main reasons for such growth is that for many organizations, AI is viewed as the solution to a lot of the uncertainty bringing improved efficiency, differentiation, automation, and reduced cost. While the 2023 economic environment remains uncertain, AI will certainly be an area of investment for organizations looking to drive automation and efficiency. In this article, we will take a look at some of the AI trends you need to be on the lookout for in 2023 and the data annotation required to make the technology a reality.
AI for Text, Speech, and Vision Will Start to Become Mainstream
There are many ways companies will increase their use of text, speech, and vision AI to achieve business growth. For example, when your sales reps are meeting with other companies via Teams or other virtual meeting software, the system produces transcripts of the call. This information can be used by AI to provide some insights into ways you can improve sales meetings. The same is true for support calls since the AI can help companies identify frequently asked questions and create proper self-service channels for them, increasing customer engagement and identifying and prescribing opportunities for cross-selling and upselling and an abundance of other allied opportunities.
In terms of speech AI, we can already see the headlines technologies like Chat GPT are making. You can expect these types of models to become even more sophisticated since their capabilities will be extended to process different semantic similarities and contextual relationships and improve upon existing applications in text summarization and generation, chatbots, increasing translation accuracy, and enhancing sentiment mining, search, code generation, etc.
In the field of computer vision, newer and more powerful models for object detection, segmentation, tracking, and counting are being built that deliver previously unimagined levels of accuracy. Augmented by extraordinarily powerful GPUs, these models will become increasingly commonplace.
Generative AI Will Play a Bigger Role in the Creative Space
Attracting and retaining the mindshare of your customer base is a problem a lot of companies are struggling with. After all, it can be difficult to produce quality content that is engaging and can be circulated on a wide variety of channels. This is where generative AI can be of great assistance since it can help companies augment their content creation. The way generative AI models work is that you provide them with a prompt of the type of image you would like to create, and the system will render abstract ideas no matter how wild or imaginative they may be. Therefore you can expect generative AI to become a big factor in increasing the innate creativity of business pursuits.
Growth of Human and Machine Collaboration
The use of intelligent machines and autonomous robots is on the rise. We can see this in places like automated distribution facilities to meet the demands of same-day deliveries, robots monitoring grocery stores for spills and stockouts, to robot arms working alongside humans on a production line. These intelligent machines are becoming more common. In fact, research from Gartner shows that, by 2030, 80% of humans will engage with smart robots on a daily basis due to smart robot advancements in intelligence, social interactions, and human augmentation capabilities, up from less than 10% today.
Enhancing Customer Experiences Both In-Store and Online With AI
With AI, you can go beyond predicting what someone might like based on past purchases and use information gained from browsing behavior or social networks to personalize recommendations for each customer. The role of AI in eCommerce is to provide support to businesses in the field of marketing, customer service, and sales. AI, intelligent machines, computer vision, image classification, deep learning, and face recognition are changing the landscape of eCommerce.
In terms of in-store shopping, retailers will focus on Frictionless shopping experiences built on computer vision and edge-based AI systems that reduce wait times and reduce hassle will be one major growth area. The future retail stores will also be able to offer hyper-personalized recommendations and craft seamless customer journeys based on real-time insights generated through video analytics powered by on-prem infrastructure.
Growth of Edge AI
Edge AI is the deployment of AI applications in devices throughout the physical world. It’s called “edge AI” because the AI computation is done near the user at the edge of the network, close to where the data is located, rather than centrally in a cloud computing facility or private data center. Until now, AI has operated almost exclusively in the cloud. But increasingly diverse streams of data are being generated around the clock from sensors at the edge. These require real-time inference, which is leading more AI deployments to move to edge computing. For airports, stores, hospitals, and more, AI brings advanced efficiency, automation, and even cost reduction, which is why edge AI adoption accelerated last year.
Increased Demand for Autonomous Vehicles
According to recent data, the AI in automotive market will expand at over 55% CAGR from 2023-2032. Rising adoption of autonomous vehicles will drive the growth of the industry. Nowadays, more and more consumers are aware of the benefits offered by autonomous vehicles with some of the world’s leading car manufacturers developing tools for self-driving cars. In addition to this, several governments and automakers across the world have begun undertaking initiatives to facilitate innovation in the autonomous vehicle landscape. All of this demand for autonomous vehicles will also result in increased demand for data annotation methods needed to train the AI vehicles. This includes things like semantic segmentation, lines and splines, 3D boxes and many more.
More Attention Will Be Given to Ethics in AI
As AI technology continues to advance, companies are starting to think about the ethical implications of these technologies. In fact, a study by IBM shows that 85% of consumers say that it is important for organizations to factor in ethics as they use AI to tackle society’s problems. One of the things you can expect to see is greater scrutiny given to the datasets used to train AI systems. An unbiased dataset is an important prerequisite for an AI model to make reliable and nondiscriminatory predictions. For example, AI models are being used for credit scoring by banks, resume shortlisting and in some judicial systems; however, it has been noticed that in some cases, the datasets had some inherent bias in them for color, age and gender.
What Types of Data Annotation are Required to Create the AI Products of Tomorrow?
The accuracy of the AI systems we talked about in this article depends on the quality of the data annotation used to train them. For example, if we look at the first trends we looked at with text, speech, and vision AI, there are so many different types of data annotation required for each of these types of AI. For text AI, the training data will need to be annotated with methods like semantic segmentation, which attach various tags to text that reference concepts and entities, such as people, places, or topics.
For speech AI, audio training data may be used, which means that speech labeling will be needed which separates the requested sounds from a given recording and tag them with keywords. Speech labeling helps in developing chatbots that perform a specific repetitive task. Natural language utterance may be necessary, which is annotating human speech to classify minute details such as intonation, dialects, semantics, context, and intonation.
For computer vision, various types of data annotation are necessary, such as object detection, where the data annotators identify the presence, location, and number of one or more objects in an image and label them accurately. Semantic segmentation will also be necessary, which delineates boundaries between similar objects and labels them under the same identification.
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January 19th, 2023Mindy News Blog