Despite decades of artificial intelligence, artificial intelligence trends continue to evolve rapidly. With the recent rapid growth of generative AI and AI-powered automation, AI evolution appears to be doubling or even more.
In this article looking at today’s AI trends, you’ll learn about some of today’s major artificial intelligence trends and how new technologies, capabilities, and use cases are impacting AI users, from the average consumer to global enterprise IT teams. Consider what kind of impact it will have.
Key trends in artificial intelligence: table of contents
1. Generative AI is already popular and gaining traction
Generative AI has taken the tech world and the planet at large by storm over the past few months, providing 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, and a close partnership with Microsoft, but other competitors are quickly catching up. For example, Google built the Google Bard feature, which is rapidly gaining traction.
Dozens of generative AI startups Already claiming rights to specific niche markets, Generative AI Enterprise Use Casedrug discovery/design, risk management and many more are set to enter the generative AI market in the coming months.
However, it is important to note that the majority of these generative AI companies fine-tune or rely on third-party underlying models rather than building their own infrastructure. In the near future, we expect the generative AI market to begin to consolidate, with leaders such as Google, Microsoft, OpenAI, and possibly Amazon vying for the position of preferred provider of both underlying models and AI assistant tools.
Additionally, more information is expected from infrastructure, hardware and computing providers such as Nvidia and Intel. The chips and GPUs they provide are finite and lucrative resources needed to power large-scale generative AI models.
See also: 100+ Top AI Companies for 2023
2. Expansion of embedded AI and UX-focused AI
Many AI companies and startups offer AI models that can be fine-tuned and incorporated into third-party systems. Using these models, a company can focus on AI-powered search, assistance, and other aspects of his UX in everything from internal employee databases to search bars and knowledge bases on external-facing websites. You will be able to create unique experiences.
The leading AI unicorn in this space is Glean, which provides generative AI solutions primarily for internal workplace app search. With solutions like Glean, companies can simplify employee onboarding and ongoing training, allowing users to easily find the documents, conversations, and other resources they need with a simple search feature. Become.
Beyond the startup space and internal enterprise use cases, both Microsoft and Google are committed to building effective AI assistants into their respective search engines.
As UX-driven AI continues to grow, AI companies will focus more on global reach and multilingual capabilities. Some AI tools currently do not work well with non-English queries. However, many companies are now building AI model training processes and global datasets to enable natural language processing and understanding in dozens of languages.
A good example of this effort comes from Cohere, a generative AI unicorn that has released products like Embed that can take text in over 100 languages and translate it.
See also: Top Generative AI Apps and Tools
3. Enhanced expectations of compliance and ethics
Artificial intelligence tools continue to mature and enter new areas of our lives, relying on large amounts of personal and sensitive data to run efficiently. But businesses and individuals alike are increasingly concerned about what data is collected, how it is used, whether it is adequately protected during use and disposed of after use.
That’s why AI companies are now being asked to make the data collection and model training process more transparent, giving users visibility into how their data is being used. Many customers also want explainable AI. These are tools and documentation that clearly explain how to optimize model performance and better analyze or fine-tune model behavior.
In response to user concerns, companies like OpenAI are trying to more clearly delineate their methodologies and internal practices around model training and data security. This expectation will only increase, especially as various technology leaders, countries and individual consumers criticize these vendors and question their overall commitment to compliance, data governance, security and ethical use.
Speaking of ethical use, tech experts and environmental activists are beginning to debate the environmental impact of the latest AI models. Many of these tools require enormous amounts of computing, both for initial training and ongoing use. This energy use leaves a significant carbon footprint that dwarfs the environmental impact of most other modern technologies.
This is less of an issue when generative AI tools and other modern models are used on a small scale, but most companies choose to use these models the way they are used, so companies are more likely to be impacted. Environmental impacts must be addressed immediately before obtaining hand.
Proposed AI regulations include: EU AI lawAs . would need to
learn more: Generative AI Ethics: Concerns and Solutions
4. Continued AI Democratization and Pervasive AI Access
Companies typically have large amounts of data to process, but few resources required to process more complex data in various formats.
Additionally, due to widespread technical talent shortages and skills gaps, many companies lack sufficient skilled staff to collect, interpret, analyze, and apply business intelligence and data to large-scale operational workflows. not here.
To address this skills shortage, many companies are turning to low-code/no-code technologies, such as user-friendly AI tools that can sift through and interpret large amounts of structured, unstructured, and semi-structured data. building or investing. These emerging low-code/no-code AI solutions are becoming increasingly important for democratized business intelligence. decision intelligencedata analysis.
Companies like DataRobot, H2O.ai, Sisu Data, and Tellius are currently building AI-driven analytics and decision intelligence solutions that lower the barrier to entry for non-data scientists. These solutions help businesses expand their data analysis capabilities and help new users better understand and contextualize their business data.
Many AI and data analytics companies are already working to improve accessibility for non-technical users, but we see more companies leaning toward low-code/no-code AI to foster democratization. would be interesting. These companies aren’t just making their tools easier to use, they’re also starting to attract new customers. We do this by integrating AI-driven intelligence into the tools you already use, such as data lakes, databases, and BI dashboards.
Click here for details: The Generative AI Landscape: Current and Future Trends
5. New cybersecurity solutions powered by AI
AI has been embedded in some cybersecurity solutions for at least a few years, but AI-powered cybersecurity tools are rapidly growing in popularity as their capabilities expand.
Network Detection and Response (NDR) and Extended Detection and Response (XDR) vendors continue to add AI-driven threat detection to their solution portfolios, helping security teams identify and respond to issues such as signatureless attacks. , helping to automate various aspects of the detection and response workflow.
Vulnerability management, penetration testing, breach and attack simulation (BAS) tools are also starting to rely heavily on artificial intelligence to more realistically simulate advanced persistent threats (APTs).
And as generative AI matures, a whole new kind of AI-powered security emerges. Google, Microsoft, CrowdStrike, Cisco, SentinelOne, and more are now using generative AI to further advance intelligent threat detection, behavioral analysis, natural language-driven querying and security analytics.
While it is true that AI-powered cybersecurity tools can be created and used by malicious actors, cybersecurity companies that choose to incorporate AI into their tools and workflows will be better equipped to address these emerging threats. in the best position to do so.
More on this topic: Generative AI and Cybersecurity
6. Computer vision and hyperautomation in manufacturing
Computer vision, a form of AI that enables computers to better understand image-based data and scenarios, has become a key part of modern manufacturing simplification and automation.
Manufacturing tasks currently handled by computer vision and related AI solutions 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 enable them to handle human-level quality assurance tasks and, in some cases, can replace the vision and skills that regular humans can bring to these tasks. increase.
Modern multimodal AI models and robotics are becoming especially important for manufacturing hyper automationenables businesses to use image inputs to obtain detailed classification, description and advice outputs. From there, users can either fix the detected issues manually or leverage Robotic Process Automation (RPA) for rule-based remediation.
For example, train a multimodal model to process images of an airplane propeller and quickly tell the user what type of propeller it is, what types of imperfections are affecting propeller performance/safety, and/or its location. can do. How to fix detected issues. In some cases, these AI models are integrated with automated bots that can make these corrections automatically.
Few AI models currently can handle this level of automation of manufacturing tasks, but more of these solutions that support and automate quality control processes are likely to emerge.
See also: Generative AI Companies: Top 12 Leaders
Conclusion: How AI Trends Affect 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. Besides cybersecurity and ethical concerns, it also raises serious and widespread concerns as many workers believe these new tools will take away their jobs.
While it is true that the job market is likely to change in response to these advances in AI, job opportunities are unlikely to decline and new opportunities are likely to emerge.
Companies and individuals that invest in AI-specific training and certifications will be in the most strategic position to readily use these new tools in the changing job market and global marketplace. The good news is that with an increasing focus on democratizing AI and data, barriers to entry are already low in terms of both skill and cost requirements for individuals looking to enhance their career paths with AI knowledge. It means that
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