Get the Latest Intel On Deep Learning and Machine Learning to Give Your Business a Marketplace Advantage
The 2024 AI Index Report from Stanford University’s Institute for Human-Centered Artificial Intelligence (HAI) is now available to the public. This report provides an in-depth look at the state of AI for the current year.
Through research, surveys, data analysis, and reviews of AI performance, the AI Index Report is a comprehensive document that assists relevant audiences, including the public, in a deeper understanding of this technological breakthrough.
Each audience that engages with the AI Index Report views its chapters through their unique lens. For example, companies who are advancing new AI applications may uncover insights about which industries are using AI and how they can reach new audiences.
Our deep learning AI consulting team views the AI Index Report from a particular context as well: how can we leverage what we’ve learned here to better serve our clients? As deep learning and machine learning AI consultants, what do we need to know that will empower our partners as they head into 2025?
To answer this question, we’ve identified three areas that software developers and businesses who want to create AI applications for their companies need to learn more about to take advantage of this technology going forward.
3 Insights On Machine Learning and Deep Learning AI That Will Strengthen Your AI Strategy For 2025
1. Business costs and profit margins: The AI Index Report cites a new study from McKinsey, revealing that 42% of all companies surveyed have seen drastic reductions in costs when implementing machine learning and deep learning into their business processes. The industries that saw the biggest gains? Manufacturing, risk management, and service operations.
We aren’t entirely surprised by these top three industries, given that current AI applications are particularly well-suited for activities like automation, streamlining processes, anomaly detection, and custom support experiences. As AI continues to grow, additional industries will begin to see similar gains.
Our developer and business leader clients who want to see cost reductions through the use of AI, including machine learning and deep learning, might consider putting themselves in the shoes of those industries that are now experiencing good results.
Ask yourself questions like:
What AI use cases did these industries identify early on?
Are there AI use cases, including machine learning models, that are emerging for my industry?
How can I begin experimenting with them now?
As you consider these questions, read the survey data for yourself in section 4.4 on the economy and corporate activity.
2. The emergence of multimodal models: A model’s ability to handle image, text, and even audio is an advancement in machine learning and deep learning AI. This improvement has implications for a number of industries. The medical field in particular continues to benchmark the success of AI as a diagnostic tool. A multimodal model can feasibly receive a visual, like an MRI and make an accurate diagnosis in the form of text, among many other use cases.
Developers and business leaders who envision using a model that flexibly and simultaneously deals with video and text inputs and outputs, and is equally strong at visual and text processing, now have an opportunity to capitalize on the strength of multimodal AI.
Read more about the growth of this specific technology in section 2.6 of the AI Index Report on technical performance and general reasoning. To learn more about multimodal models, read this article on visual large language models.
3. Synthetic data is trending: In the same chapter on technical performance, the report emphasizes deep learning AI’s new ability to create data for model training. Models such as “SegmentAnything” from Meta AI can cut out any part of an image without additional training on the image. These one-click, zero-shot capabilities come courtesy of synthetic data training, which not only reduces training time for new applications, but promises a feedback loop in which AI can teach itself to improve.
Our clients working on applications and experiencing bottlenecks in the training process now have solutions with the generation of data that is customized for their use case. Read more about this opportunity in section 2.7 of the report.
Conclusions On Machine Learning and Deep Learning For 2025
Whether you are considering the development or utilization of an AI application, or you plan to do so in the coming year, there are rich opportunities to capitalize on, so your work remains cutting edge and relevant.
These reflection questions will help you plan your AI strategy for 2025, based on our three takeaways from this year’s AI Index Report.
What breakthroughs, including synthetic data, can improve and enhance our deep learning and machine learning models?
Certain industries are getting a great deal of value out of AI. Where can we use current AI applications, such as anomaly detection? What technologies should we be looking at that may become more available to our industry?
Models with multimodal capabilities are increasingly more available. What areas of our business could be improved with generative AI (inputting video or audio to receive a text output, or inputting text to receive an audio or video output)?
As you continue to decide on your business’ next steps for machine learning and deep learning AI, explore more specific topics related to AI in this developers’ resource hub.
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