Artificial intelligence (AI) and machine learning (ML) have become increasingly prevalent in recent years. However, distinguishing between the two and deciphering their impact can be challenging. Pedro Rodriguez, an expert in both AI and ML, shed some light on these concepts during a Breaking Defense webinar. The discussion aimed to highlight the current practical applications of AI/ML tools, rather than speculating about their future possibilities. Joining Rodriguez were Andrew Pepler, a senior strategy and policy analyst, and Vivek Chilukuri, a senior fellow.
According to Rodriguez, AI refers to machine-based systems capable of predicting, recommending, or influencing real or virtual environments. In essence, AI involves generating ideas and actions. However, the term “AI” encompasses a broad range of definitions. Rodriguez personally favors the concept of machine learning, which can be seen as a subset of AI. Machine learning allows computers to learn from data without explicit programming, relying on data-driven approaches instead. As the principal AI technical leader at the Applied Physics Laboratory, Rodriguez believes that the clear definition of machine learning is more beneficial.
Despite machine learning having been around for fifty years, it was primarily focused on transferring human knowledge into machines during most of that time. The current AI boom, however, is distinct. Deep learning, a driving force behind this boom, involves training computers on vast amounts of data and imagery. The goal is for computers to detect meaningful patterns within these inputs independently. The massive amounts of data processed by computers, commonly referred to as Large Learning Models, have revolutionized the field.
During the webinar, Pepler, the lead author of the Department of Defense’s recently released Data Analytics and AI Adoption Strategy, discussed several topics. One of these was the areas where AI has already made significant progress, such as organizing data for analysts and AI models. Additionally, the conversation delved into the evaluation and testing of AI systems and the challenges of regulating AI in an era where even social media and cryptocurrency have evaded comprehensive regulation by Congress.
If you are interested in learning more, you can watch the full free webinar on-demand [here](https://breakingdefense.com).
FAQs
1. What is artificial intelligence (AI)?
AI refers to machine-based systems that can make predictions, recommendations, or decisions influencing real or virtual environments. It involves formulating ideas and actions.
2. How does machine learning differ from AI?
Machine learning is a subset of AI that allows computers to learn from data without explicit programming. It relies on data-driven approaches.
3. What is the significance of deep learning in the current AI boom?
Deep learning involves training computers on vast amounts of data and imagery, enabling them to identify meaningful patterns independently.
4. What are some practical applications of AI?
AI is gaining traction in various areas, such as organizing data for analysts and AI models.
5. Are there any challenges in regulating AI?
Regulating AI presents challenges, especially considering the limited understanding of its impact among lawmakers, as demonstrated by the struggles in regulating social media and cryptocurrency.
Key Terms
– Artificial intelligence (AI): Machine-based systems capable of predicting, recommending, or influencing real or virtual environments.
– Machine learning (ML): Subset of AI that allows computers to learn from data without explicit programming.
– Deep learning: Involves training computers on vast amounts of data and imagery to detect meaningful patterns independently.
– Large Learning Models: Massive amounts of data processed by computers in AI systems.
Related Links
– For more information on AI and machine learning, you can watch the full free webinar on-demand here.
The source of the article is from the blog mivalle.net.ar