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10 Top AI and machine learning trends for 2023

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Where does AI currently stand?

Artificial intelligence technology is revolutionizing our businesses and societies alike, so which developments should companies keep an eye on in 2023?

Success stories for ChatGPT centre on its accomplishments and advancement in algorithm development. As an innovative AI technology capable of revolutionizing search engines, ChatGPT stands out.

Corporate executives would do well to recognize and appreciate innovative machines that automate machine learning pipelines, significantly expediting development processes and speeding up development times.

Additionally, AI is quickly expanding into new fields such as conceptual design for smaller devices and multi-modal applications – developments which will broaden AI’s reach across many industries. Businesses should stay abreast of cutting-edge AI technologies which show promise such as cloud and quantum AI testing.


What are AI and machine learning trends for 2023?

To take full advantage of artificial intelligence (AI) and machine-learning developments, IT and business leaders must devise plans to align AI with the employee needs and goals of their businesses. A discussion agenda should cover:

  • How can we simplify and expand access to AI;
  • how can we address ethical/responsible AI;
  • how to connect AI compensation with business goals in order to make sure AI implementations live up to expectations.


Here are 10 top 2023 trends IT leaders should prepare for:


1. Automated machine learning (AutoML)

Michael Mazur, CEO of AI Clearing’s software firm which utilizes artificial intelligence (AI) to enhance construction reports, notes two exciting aspects of machine learning automation: more effective methods for labelling data and automated adjustments of neural net structures.

Mazur explained that data labelling led to the creation of an industry employing human annotators in low-cost economies such as India, Central Eastern Europe and South America. To avoid or minimize using workers from offshore “pushed markets to find ways of avoiding or minimizing this portion of the method.” As advances in semi and self-supervised education help companies minimize manually labelled data to an absolute minimum level.

Automating the selection and adjustment process for neural network models makes AI cheaper while innovative solutions become readily available for sale sooner.

Gartner believes the future will see increased emphasis on improving PlatformOps, MLOps and DataOps processes to better operate the models they describe.


2. AI-enabled conceptual design

AI technology was traditionally utilized for automating processes involving images, data analysis and the interpretation of language.

Recently, OpenAI developed two models – CLIP and DALL*E (Contrastive Language Learning with Images), to address tasks requiring repetitive actions in retail, finance, healthcare or the arts sectors. Both these models combine images and language into dynamic designs from an explanation of text.

Initial research illustrates how models can be trained to create new designs. One such design was an avocado-shaped armchair developed after giving its artificial intelligence system the name “avocado chair.” Mazur believes the latest AI models will facilitate mass adoption into creative industries like fashion, architecture and other creative sectors: “Soon we could see something similar disrupt fashion, architecture or other forms of creative endeavours,” according to him.


3. Multi-modal learning

AI is becoming more advanced at integrating multiple modalities into its models, including vision, text speech and IoT sensor information. Google DeepMind recently made waves with Gato – an AI model capable of understanding language visualization as well as robotic movements.

David Talby, founder and CTO of John Snow Labs (an NLP tools provider), notes that developers are finding innovative ways to combine different methods in order to streamline tasks like understanding documents more quickly.


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4. Models that can achieve multiple objectives

AI models often begin with one goal in mind, such as maximizing revenues. As their initial efforts progress and mature, firms will likely invest in models with multiple goals at the forefront, according to Justin Silver, AI strategist and data science manager of PROS’ AI-powered sales management platform. Multi-task models differ from multi-modal ones in that the latter attempt to establish a standard representation across various forms of data.

One business metric without taking into account other goals will produce suboptimal outcomes, for instance a product recommendation engine which solely considers sales conversion rates could miss opportunities to increase revenues due to new or unique products not purchased before by customers. Furthermore, due to ESG goals being increasingly recognized within businesses today it requires Chief Information Officers (CIOs) to create models which fulfill sustainability objectives such as circularity and carbon reduction alongside traditional business objectives such as inventory reduction, delivery time reduction and cost control goals.


5. AI-based cybersecurity

Innovative AI and machine-learning techniques will become increasingly vital to discovering and mitigating cybersecurity threats, according to Ed Bowen, advisory AI Director and leader at Deloitte. One major reason is that adversaries use these methods of analysis in order to discover vulnerabilities within networks.

Ed Bowen anticipates that more companies will utilize AI as both a proactive and defensive measure, to detect suspicious activities and patterns of attack. Businesses which don’t integrate AI are likely to fall behind security trends, increasing their risks with potentially disastrous repercussions.

“AI-supported cyber applications tend to be better equipped at handling multiple dynamic risks by increasing detection efficiency and providing more resilience and agility when faced with disruption,” according to Bowen.

Businesses that fail to implement AI could fall further behind in security matters and experience more negative repercussions, according to Hettmans.


6. Improved language modeling

ChatGPT provided an innovative new way of connecting with AI through an engaging interface, making the technology suitable for use across a range of fields such as marketing, customer service automation and user experience management.

By 2023, you should anticipate an increase in demand for quality assurance aspects of these enhanced AI models of language. Protests against inaccurate results already abound against their coding; and companies will need to deal with criticism surrounding inaccurate product descriptions as well as unsafe advice in coming years – leading to increased need for methods that better explain where mistakes arise.


7. Computer vision in business expands but ROI a challenge

Cameras that are less costly and the development of AI will fuel an explosion of computers using vision to automate and analyze in 2023.

Access to computing sensors, data analytics and advanced vision models is creating new opportunities to automate processes which require humans to examine and comprehend things in our real environment,” stated Scott Likens of PwC’s Tech Group as their Tech Leader for Trust and Innovation.

Back office operations benefiting from enhanced machine vision can enhance document workflow processes. Meanwhile, computer vision will digitalize physical elements of business processes.

Likens predicts CIOs will face difficulty realizing returns from initiatives they undertake, with finding appropriate use cases being key to earning returns on them. He anticipates an increase in demand for “bilinguals”, or people capable of crossing over between business and technology to identify new opportunities within computer vision.

Implementing computer vision requires specialized skills. Highly efficient systems require thousands of identified examples that may not exist within an organization and must be manually managed at considerable expense, creating an economic barrier to entry. Computer vision applications present additional complications not usually experienced when applying deep learning models to forecast or language tasks; special equipment such as cameras or edge computing capabilities might be required depending on their use case and this requires new infrastructure management capabilities within businesses that don’t already possess this type of infrastructure in their tech ecosystem.


8. Democratized AI

AI tools are making AI creation simpler, with less knowledge needed to construct models. This makes incorporating experts easier than ever into the process, speeding up development while increasing accuracy by engaging subject experts on site, Talby noted. Frontline experts can identify models which provide maximum value while pinpointing problematic ones or areas needing workarounds.

Doug Rank, data scientist at PS AI Labs, notes this trend will mirror that of networks and computers when first adopted within businesses – starting as tools used by only a select few experts before becoming widespread adoption across enterprises. Doug predicts the primary challenge will be protecting access to this data through adequate security measures.

“With careful preparation, IT leaders can ensure their data remains reliable and up-to-date during cloud migrations, taking full advantage of AI.” Rank stated.

According to Pini Solomovitz, Head of Innovation and Development at GPU Orchestration System Run:ai (Run:ai), attempts at making AI tools simpler for users can lead to non-standard AI applications being utilized that fall outside existing IT services – an issue known as shadow AI which mirrors other forms of shadow IT that often arise due to cheap cloud services.

AI will democratize our systems, but comes at a cost in terms of privacy, ethics and business consequences. CIOs will need to assess newer applications of AI in order to reduce costs, identify risks and simplify workflows for their organization.


9. Bias removal in ML

As AI adoption in business grows, affecting more people each day, its implementation must not lead to discrimination and unfairness in its predictions – making sure no one is disfavored when seeking loans, purchasing products online or receiving medical care. It is vitally important that AI can predict in an equitable way so as to guarantee equal treatment when seeking loans, purchasing products online or receiving care.

Liran Hason of Aporia AI explanation platform made this comment. With companies’ reputations at stake, minimizing bias in machine learning solutions is essential in order to build trust in them.

CIOs in 2023 will face challenges when it comes to overseeing data science and models built with machine learning (ML), due to the complex nature of these models. Implementation of accountable AI methods and providing their company with appropriate tools will become even more crucial, according to Hason’s forecasts. Hason predicts an increase in interest regarding monitoring tools that reduce bias while production AI creates accurate predictions, with monitoring tools capable of pinpointing specific features which contributed to an incorrect prediction being developed further by AI production systems.


10. Digital twins drive the industrial metaverse

Since last year, leading manufacturers of industrial design and AI have filled in gaps through digital twins – virtual models that replicate reality – and the metaverse. Nvidia and Siemens joined forces to create an industrial metaverse while Bentley, a construction firm has created what they have named an infrastructure metaverse.

Anand Rao, global AI lead at PwC, believes these developments could mark an important turning point for digital twins, from vague technologies into essential parts of IT strategy. Digital twins have already seen usage across numerous industries over recent years and he expects that their use will only accelerate by 2023.

Evidently, digital twins have grown more complex and sophisticated over time, evolving from relatively basic synthetic or real-time data-based digital twins to asset-based IoT twins to ecosystem and customer based digital twins that model ecosystems and customers with customer models incorporated. Furthermore, experts report that these models can now be utilized to simulate human behaviour and analyze possible future scenarios; leading them to be integrated into traditional industrial simulations as well as artificial intelligence agents-based simulation.

“The next phase of this process entails merging science computing industrial simulation, artificial intelligence and simulation intelligence in order to develop simulation intelligence,” Rao noted. This will integrate foundational elements of simulation within operating systems and bring new forms of knowledge about them into use for decision-making purposes.

Digital twins present businesses with exciting possibilities to forecast and utilize data. Businesses using more advanced and flexible digital twins can use simulation intelligence to predict real world scenarios like disease progression, customer behavior and economic impacts of pandemics. Digital twins may become essential tools in ESG modeling/intelligence city developments/drug design applications and other fields.


Pilot stage digital twin projects are increasingly being scaled-up and operationalized, prompting CIOs to consider ways in which they can incorporate them into the overall architecture of analytics for their business and cloud/IT stack. Companies must offer both development environments for simulation development as well as production environments to run simulations; simulations also necessitate on-demand computing power from either their on-premise servers or the cloud.

CIOs use virtual reality (VR) models as an invaluable way to train employees. Businesses should devise an established procedure for scoping, designing, calibrating, deploying, monitoring and scoping digital twins – digital twins could play an instrumental role in helping CIOs transform businesses if all participants involved are prepared to utilize these virtual environments

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