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Artificial intelligence trends continue to rapidly evolve, even though AI has now been around for decades. With the recent rapid growth in generative AI and AI-powered automation, AI evolution seems to be moving at double speed – or faster.
In this look at today’s AI trends, learn about some of today’s top artificial intelligence trends and consider how emerging technologies, capabilities, and use cases will impact AI users, from the average consumer to the global enterprise IT team.
Leading Artificial Intelligence Trends: Table of Contents
- Generative AI’s Grows Its Already-Popular Presence
- Embedded AI and UX-Focused AI Expand
- Stronger Compliance and Ethics Expectations
- Continued AI Democratization and Widespread AI Access
- New AI-Powered Cybersecurity Solutions
- Computer Vision and Hyperautomation in Manufacturing
- Bottom Line: How AI Trends Impact You and Your Business
1. Generative AI Grows Its Already-Popular Presence
Generative AI has taken the tech world and the greater globe by storm over the past several months, offering user-friendly AI models for text, image, audio, and other forms of data generation. OpenAI currently dominates the generative AI scene with solutions like GPT-4 and ChatGPT, as well as its close partnership with Microsoft, but other competitors are quickly catching up: Google is building out Google Bard’s capabilities and quickly gaining traction, for example.
Dozens of generative AI startups are already staking their claim to specific niche markets and generative AI enterprise use cases, like drug discovery/design and risk management, and it’s clear that many more will enter the generative AI market over the coming months.
However, it’s important to note that the majority of these generative AI companies are fine-tuning or otherwise relying on third-party foundation models rather than building their own infrastructure. In the near future, expect the generative AI market to start to consolidate, with leaders like Google, Microsoft, OpenAI, possibly Amazon, and others vying for preferred provider spots for both foundation models and AI assistant tools.
Additionally, expect to hear more from infrastructure, hardware, and compute providers, such as Nvidia and Intel; the chips and GPUs they provide are finite, lucrative resources that are necessary for powering generative AI models at scale.
Also see: 100+ Top AI Companies 2023
2. Embedded AI and UX-Focused AI Expand
A number of AI companies and startups offer AI models that can be fine-tuned and embedded into third-party systems. These models make it possible for businesses to create AI-powered search, assistance, and other UX-focused experiences in everything from internal employee databases to external-facing website search bars and knowledge bases.
A leading AI unicorn in this area is Glean, which primarily offers generative AI solutions for internal workplace app searches. Using solutions like Glean, businesses can simplify onboarding and ongoing training for employees, making it easy for users to find the documents, conversations, and other resources they need with a simple search function.
Beyond the startup space and internal enterprise use cases, Microsoft and Google are both working hard to incorporate effective AI assistants into their respective search engines.
As UX-driven AI continues to grow, AI companies are likely to focus more heavily on their global footprint and multilingual capabilities. Some AI tools currently don’t work well beyond English-language queries. However, a number of companies are currently building out their AI model training processes and global datasets to make natural language processing and understanding possible for dozens of languages.
A good example of this effort is coming from Cohere, a generative AI unicorn that has released products like Embed, which can retrieve and translate text in over 100 languages.
Also see: Top Generative AI Apps and Tools
3. Stronger Compliance and Ethics Expectations
Artificial intelligence tools continue to mature and reach into new areas of our lives, relying on massive amounts of personal and sensitive data to run effectively. But businesses and individuals alike are growing concerned about what data is collected, how it’s used, and whether or not it is appropriately secured during use and disposed of after use.
As such, there’s currently a push for AI companies to make their data collection and model training processes more transparent so users know how their data is being used. Many customers are also pushing for explainable AI. These are tools and documentation that clearly explain how to optimize model performance and better analyze or fine-tune model behaviors.
In response to user concerns, companies like OpenAI are attempting to more clearly delineate their methodologies and internal practices for model training and data security. This expectation will only grow, especially as various tech leaders, countries, and individual consumers call out these vendors and question their overall commitments to compliance, data governance, security, and ethical use.
Speaking of ethical use, tech experts and environmentalists are beginning to discuss the environmental impact of the latest AI models. Many of these tools require enormous amounts of compute, both for initial training and ongoing use. This energy usage leaves behind a considerable carbon footprint that dwarfs most other modern technologies’ environmental impact.
When generative AI tools and other modern models are used on a smaller scale, this is less of a problem, but with the way most enterprises are choosing to use these models, the environmental consequences will need to be addressed soon before they get more out of hand.
As proposed AI regulations, such as the EU’s AI Act, are pushed forward, AI companies will need to justify the tools they’re creating and what they do – and also the materials they use, the energy they consume, and the security and compliance safeguards they put in place to protect consumers.
Learn more: Generative AI Ethics: Concerns and Solutions
4. Continued AI Democratization and Widespread AI Access
Businesses typically have massive amounts of data to process but few of the resources they need to process more complex data in different formats.
Additionally, with a widespread tech talent shortage and skills gap, many businesses do not have enough skilled staff to collect, interpret, analyze, and apply business intelligence and data to their operational workflows at scale.
To combat this skills shortage, a number of businesses are building or investing in low-code/no-code technology, including user-friendly AI tools that can sift through and interpret large quantities of structured, unstructured, and semi-structured data. These emerging low-code/no-code AI solutions are becoming increasingly important for democratized business intelligence, decision intelligence, and data analytics.
Companies like DataRobot, H2O.ai, Sisu Data, and Tellius are currently building out AI-driven analytics and decision intelligence solutions that lower the barrier of entry for non-data scientists. These solutions help businesses expand their data analysis capabilities and new users better understand and contextualize business data.
While a large number of AI and data analytics companies are already working to improve their accessibility for less-technical users, it’ll be interesting to watch as more companies lean into low-code/no-code AI for increased democratization. Beyond simply making these tools easier to use, these companies are also starting to win over new customers. They’re doing this by integrating AI-driven intelligence into the tools they already use, including data lakes and databases, BI dashboards, and more.
Learn more here: Generative AI Landscape: Current and Future Trends
5. New AI-Powered Cybersecurity Solutions
AI has been incorporated into some cybersecurity solutions for at least a few years now, but AI-powered cybersecurity tools are quickly becoming more popular as they expand their capabilities.
Network detection and response (NDR) and extended detection and response (XDR) vendors continue to add AI-driven threat detection to their solutions portfolios, helping security teams identify and address issues like signatureless attacks and automate different aspects of their detection and response workflows.
Vulnerability management, pentesting, and breach and attack simulation (BAS) tools are also beginning to rely heavily on artificial intelligence in order to more realistically simulate advanced persistent threats (APTs).
And a brand new kind of AI-powered security has emerged with the maturation of generative AI. Google, Microsoft, CrowdStrike, Cisco, SentinelOne, and many others are now using generative AI to further advance intelligent threat detection, behavioral analysis, and natural-language-driven queries and security analytics.
Certainly, AI-driven cybersecurity tools can be created and leveraged by bad actors, but the cybersecurity companies that choose to incorporate AI into their tools and workflows now are best positioned to handle these emerging threats.
More on this topic: Generative AI and Cybersecurity
6. Computer Vision and Hyperautomation in Manufacturing
Computer vision, a type of AI that makes it possible for computers to better understand image-based data and scenarios, has become a key part of simplifying and automating modern manufacturing.
Manufacturing tasks that computer vision and related AI solutions are currently handling include automated product defect detection, 3D modeling, risk management, product counting and packaging support, predictive maintenance, and inventory management. The visual processing capabilities of these computer vision tools give them the ability to handle human-level quality assurance tasks and, in some cases, to supersede the vision and skills a typical human could bring to these tasks.
The latest multimodal AI models and robotics have become particularly important to manufacturing hyperautomation, allowing companies to use image inputs to get detailed classification, explanation, and advice outputs. From there, users can either manually correct any detected problems or rely on robotic process automation (RPA) to make rule-based fixes.
For example, a multimodal model could be trained to process an image of an airplane’s propeller and quickly tell users what kind of propeller it is, the types of defects that are affecting the propeller’s performance/safety and where they’re located, and/or how to correct any detected issues. In some cases, these AI models are integrated with automated bots that can make these corrections automatically.
AI models that are able to handle this level of manufacturing task automation are currently few and far between, but more of these solutions will likely emerge to support and automate the quality control process.
Also see: Generative AI Companies: Top 12 Leaders
Bottom Line: How AI Trends Impact You and Your Business
Artificial intelligence solutions themselves are changing rapidly, and with that change comes new opportunities to make AI relevant and accessible to new audiences. There also comes profound and widespread anxiety, not only due to cybersecurity and ethical concerns but also because many workers believe these new tools will take their jobs.
While it’s true that the job market will likely change in response to all of these AI advancements, it’s less likely that job opportunities will decrease and more likely new opportunities will emerge.
The companies and individuals that invest in AI-specific training and certifications will find themselves in the most strategic position, ready and able to use these new tools in a changing job market and global marketplace. The good news is that the increasing emphasis on AI and data democratization is already lowering the barrier of entry — both in skills and cost requirements — for individuals who want to bolster their career paths with AI knowledge.
Read next: Best Artificial Intelligence Software
