AI Research

Artificial Intelligence – #1 Guide for Everyday People

Published

1 month ago

August 12, 2025

Smith

Artificial Intelligence Explained: A Complete Guide for Everyday People

Introduction: What is Artificial Intelligence?

(STL.News) Artificial Intelligence, or AI, is a branch of computer science that focuses on building machines and software capable of performing tasks that typically require human intelligence. These tasks include understanding language, recognizing images, making decisions, learning from experience, and solving problems.

Think of AI as the ability for computers to “think” and “learn” in ways that mimic human reasoning—although not exactly the same as human thought. While AI doesn’t have emotions or consciousness, it can process massive amounts of information much faster than humans, identify patterns, and make predictions based on data.

From voice assistants like Siri and Alexa to recommendation systems like Netflix and YouTube, AI is already embedded in our daily lives—often without us even realizing it.

A Brief History of Artificial Intelligence (AI)

While AI feels like a new invention, the concept has been around for decades.

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1950s – The Birth of the Idea
British mathematician Alan Turing asked, “Can machines think?” He developed the Turing Test to determine whether a machine could convincingly imitate human conversation.
Around this time, the term Artificial Intelligence was coined by John McCarthy in 1956 during the famous Dartmouth Conference.
1960s–1980s – Early Experiments
AI researchers have created simple programs that can solve puzzles, play chess, and perform basic problem-solving tasks. However, computing power was limited, and progress was slow.
1990s – AI Gets Smarter
IBM’s Deep Blue defeated world chess champion Garry Kasparov in 1997. This proved that AI could outperform humans in specialized tasks.
2000s–Present – AI in Everyday Life
Faster computers, big data, and advances in machine learning have made AI mainstream. Today, AI powers self-driving cars, language translation apps, fraud detection systems, and even medical diagnoses.

The Main Types of Artificial Intelligence (AI)

AI can be categorized in several ways, but here are the most common classifications:

1. Narrow AI (Weak AI)

Definition: AI that is designed for one specific task.
Example: Google Search, facial recognition, or spam filters in your email.
Key Point: Narrow AI is incredibly effective at its task but cannot perform any actions outside its programmed scope.

2. General AI (Strong AI)

Definition: AI that can perform any intellectual task a human can do.
Example: This type of AI doesn’t truly exist yet—it’s the kind we see in movies like Her or The Matrix.
Key Point: Achieving General AI would mean creating machines with human-like understanding and adaptability.

3. Superintelligent AI

Definition: AI that surpasses human intelligence in every aspect.
Example: A hypothetical future AI capable of outthinking humans in science, art, decision-making, and emotional understanding.
Key Point: Many experts debate whether this will happen and what ethical issues it could raise.

How Artificial Intelligence (AI) Works: The Basics

While the technology behind AI can get complex, the core concept is simple: AI learns patterns from data and uses those patterns to make decisions or predictions.

Key Components:

Data – AI needs information to learn. The more data it has, the better it can perform.
Algorithms – These are step-by-step instructions that tell the AI how to process data.
Models – Once trained on data, the AI builds a “model” that can recognize patterns or predict outcomes.
Training – AI systems “learn” by being fed data and adjusting until they produce accurate results.
Feedback Loop – AI improves over time by comparing predictions with actual outcomes and adjusting accordingly.

Machine Learning vs. Artificial Intelligence (AI)

People often confuse Machine Learning (ML) with AI. Here’s the difference:

AI is the broader concept of machines performing tasks that require intelligence.
Machine Learning is a subset of AI where machines improve their performance by learning from data—without being explicitly programmed for every step.

Example:
If AI is like teaching a child everything step-by-step, machine learning is like giving them examples and letting them figure things out for themselves.

Deep Learning: The Power Behind Modern Artificial Intelligence (AI)

Deep Learning is a more advanced subset of machine learning. It uses neural networks—systems inspired by the human brain—to process data in layers, recognizing increasingly complex patterns.

Example:
When Facebook automatically tags your friends in photos, it’s using deep learning to recognize faces.

Typical Applications of Artificial Intelligence (AI) in Everyday Life

AI is already everywhere—often in ways you don’t notice.

Voice Assistants – Alexa, Siri, and Google Assistant understand and respond to your voice commands.
Recommendation Systems – Netflix suggests shows you might like based on your viewing history.
Navigation Apps – Google Maps uses AI to suggest the fastest routes by analyzing traffic in real time.
Fraud Detection – Banks use AI to spot suspicious transactions instantly.
Healthcare – AI helps detect diseases from scans or predict health risks.
Customer Service – Chatbots can answer questions 24/7.
Social Media – AI curates your feed, detects harmful content, and recommends connections.
E-commerce – Online stores use AI to suggest products you’re likely to buy.

The Benefits of Artificial Intelligence (AI)

1. Speed and Efficiency

AI can process data and perform calculations much faster than humans.

2. Cost Savings

Automation reduces the need for human labor in repetitive tasks.

3. Accuracy

AI can detect patterns and make predictions with high precision—sometimes more accurately than humans.

4. 24/7 Availability

AI systems don’t get tired, hungry, or distracted.

5. Scalability

AI can handle massive workloads that would require hundreds of humans.

The Challenges and Risks of Artificial Intelligence (AI)

1. Job Displacement

Automation can replace human workers in some industries.

2. Bias in AI

If the data used to train AI is biased, the AI will make biased decisions.

3. Privacy Concerns

AI often requires large amounts of personal data, which raises significant privacy concerns.

4. Security Risks

Hackers could exploit AI systems to cause harm.

5. Ethical Dilemmas

Should AI be used in warfare? Should it make life-and-death decisions?

Artificial Intelligence (AI) Myths and Misconceptions

Myth: AI will take over the world, as depicted in movies.
Fact: Current AI is specialized and lacks human-like consciousness.
Myth: AI is always correct.
Fact: AI can make mistakes, especially if trained on flawed data.
Myth: AI will replace all jobs.
Fact: AI will eliminate some jobs but also create new ones.

The Future of Artificial Intelligence (AI)

Experts believe AI will continue to grow in three main areas:

Automation of Complex Tasks – AI will move beyond repetitive work into areas like legal research or advanced medical diagnosis.
Better Human-AI Collaboration – AI will become a tool that works with people rather than replacing them entirely.
Ethical AI Development – Governments and companies will work on creating guidelines to ensure AI is used responsibly.

How to Prepare for an AI-Driven Future

Learn New Skills – Focus on creativity, emotional intelligence, and critical thinking—skills AI can’t easily replicate.
Understand AI Basics – You don’t need to be a programmer, but knowing how AI works will help you adapt.
Stay Informed – Follow credible sources for AI news and updates.

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Conclusion

Artificial Intelligence is not science fiction—it’s a powerful tool shaping our present and future. From making online shopping easier to helping doctors save lives, AI has the potential to improve nearly every part of society.

However, it also raises important questions about ethics, jobs, and privacy. Understanding AI in simple terms allows us to approach it with both excitement and caution. The more we know, the better we can utilize AI to benefit humanity—without losing sight of the values that define us as human.

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AI Research

Advarra launches AI- and data-backed study design solution to improve operational efficiency in clinical trials

Published

23 minutes ago

September 17, 2025

The Editors

Advarra, the market leader in regulatory reviews and a leading provider of clinical research technology, today announced the launch of its Study Design solution, which uses AI- and data-driven insights to help life sciences companies design protocols for greater operational efficiency in the real world.

Study Design solution evaluates a protocol’s feasibility by comparing it to similar trials using Braid™, Advarra’s newly launched data and AI engine. Braid is powered by a uniquely rich set of digitized protocol-related documents and operational data from over 30,000 historical studies conducted by 3,500 sponsors. Drawing on Advarra’s institutional review board (IRB) and clinical trial systems, this dataset spans diverse trial types and therapeutic areas, provides granular detail on schedules of assessment, and tracks longitudinal study modifications, giving sponsors deeper insights than solutions based only on in-house or public datasets.

“Too often, clinical trial protocols are developed without the benefit of robust comparative intelligence, leading to inefficient designs and operations,” said Laura Russell, senior vice president, head of data and AI product development at Advarra. “By drawing on the industry’s largest and richest operational dataset, Advarra’s Study Design solution delivers deeper insights into the feasibility of a protocol’s design. It helps sponsors better anticipate downstream operational challenges, make more informed decisions to simplify trial designs, and accelerate protocol development timelines.”

Advarra’s Study Design solution can be used to optimize a protocol prior to final submission or for retrospective analyses. The solution provides insights on design factors that drive operational feasibility, such as the impact of eligibility criteria, burdensomeness of the schedule of assessment on sites and participants, and reasons for amendments. Study teams receive custom benchmarking that allows for operational risk assessments through tailored data visualizations and consultations with Advarra’s data and study design experts. Technical teams can work directly within Advarra’s secure, self-service insights workspace to explore operational data for the purpose of powering internal analyses, models, and business intelligence tools.

“Early pilots have already demonstrated measurable impact,” added Russell. “In one engagement, benchmarking a sponsor’s protocol against comparable studies revealed twice as many exclusion criteria and 60 percent more site visits than industry benchmarks. With these insights, the sponsor saw a path to streamline future trial designs by removing unnecessary criteria, clustering procedures, and adopting hybrid visit models, ultimately reducing site burden and making participation easier for patients.”

Study Design solution is the first in a series of offerings by Advarra that will be powered by Braid. Future applications will extend insights beyond protocol design to improve study startup, enhance collaboration, and better support sites.

To learn more about Study Design solution or to request a consultation, visit advarra.com/study-design.

About Advarra
Advarra breaks the silos that impede clinical research, aligning patients, sites, sponsors, and CROs in a connected ecosystem to accelerate trials. Advarra is number one in research review services, a leader in site and sponsor technology, and is trusted by the top 50 global biopharma sponsors, top 20 CROs, and 50,000 site investigators worldwide. Advarra solutions enable collaboration, transparency, and speed to optimize trial operations, ensure patient safety and engagement, and reimagine clinical research while improving compliance. For more information, visit advarra.com.

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AI Research

Best Artificial Intelligence (AI) Stock to Buy Now: Nvidia or Palantir?

Published

30 minutes ago

September 17, 2025

Keithen Drury

Palantir has outperformed Nvidia so far this year, but investors shouldn’t ignore the chipmaker’s valuation.

Artificial intelligence (AI) investing is a remarkably broad field, as there are numerous ways to profit from this trend. Two of the most popular are Nvidia (NVDA -1.55%) and Palantir (PLTR -0.58%), which represent two different sides of AI investing.

Nvidia is on the hardware side, while Palantir produces AI software. These are two lucrative fields to invest in, but is there a clear-cut winner? Let’s find out.

Image source: Getty Images.

Palantir’s business model is more sustainable

Nvidia manufactures graphics processing units (GPUs), which have become the preferred computing hardware for processing AI workloads. While Nvidia has made a ton of money selling GPUs, it’s not done yet. Nvidia expects the big four AI hyperscalers to spend around $600 billion in data center capital expenditures this year, but projects that global data center capital expenditures will increase to $3 trillion to $4 trillion by 2030. That’s a major spending boom, and Nvidia will reap a substantial amount of money from that rise.

However, Nvidia isn’t completely safe. Its GPUs could fall out of style with AI hyperscalers as they develop in-house AI processing chips that could steal some of Nvidia’s market share. Furthermore, if demand for computing equipment diminishes, Nvidia’s revenue streams could fall. That’s why a subscription model like Palantir is a better business over the long term.

Palantir develops AI software that can be described as “data in, insights out.” By using AI to process a ton of information rapidly, Palantir can provide real-time insights for what those with decision-making authority should do. Furthermore, it also gives developers the power to deploy AI agents, which can act autonomously within a business.

Palantir sells its software to commercial clients and government entities, and has gathered a sizable customer base, although that figure is rapidly expanding. As the AI boom continues, these customers will likely stick with Palantir because it’s incredibly difficult to move away from the software once it has been deployed. This means that after the AI spending boom is complete, Palantir will still be able to generate continuous revenue from its software subscriptions.

This gives Palantir a business advantage.

Nvidia is growing faster

Although Palantir’s revenue growth is accelerating, it’s still slower than Nvidia’s.

NVDA Revenue (Quarterly YoY Growth) data by YCharts

This may invert sometime in the near future, but for now, Nvidia has the growth edge.

One item that could reaccelerate Nvidia’s growth is the return of its business in China. Nvidia is currently working on obtaining its export license for H20 chips. Once that is returned, the company could see a massive demand from another country that requires significant AI computing power. Even without a massive chunk of sales, Nvidia is still growing faster than Palantir, giving it the advantage here.

Nvidia is far cheaper than Palantir

With both companies growing at a similar rate, it would be logical to expect that they should trade within a similar valuation range. However, that’s not the case. Whether you analyze the stocks from a forward price-to-earnings (P/E) or price-to-sales (P/S) basis, Palantir’s stock is unbelievably expensive.

NVDA PE Ratio (Forward) data by YCharts

From a P/S basis, Palantir is about 5 times more expensive than Nvidia. From a forward P/E basis, it’s about 6.5 times more expensive.

With these two growing at the same rate, this massive premium for Palantir’s stock doesn’t make a ton of sense. It will take years, or even a decade, at Palantir’s growth rate to bring its valuation down to a reasonable level; yet, Nvidia is already trading at that price point.

I think this gives Nvidia an unassailable advantage for investors, and I think it’s the far better buy right now, primarily due to valuation, as Palantir’s price has gotten out of control.

Keithen Drury has positions in Nvidia. The Motley Fool has positions in and recommends Nvidia and Palantir Technologies. The Motley Fool has a disclosure policy.

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