I’d say I hope for a world where technology, especially AI, is used thoughtfully to empower people rather than replace or control them. Ideally, we’d see AI helping humanity solve big challenges: making healthcare accessible, creating sustainable energy, reducing inequality, and fostering education everywhere. I think AI has incredible potential to augment human creativity, bring people together, and support a world where people have more time for connection, learning, and pursuing what truly fulfills them.
And beyond tech, I’d hope for a world where empathy, collaboration, and curiosity are at the heart of progress. It’s my goal to be part of that by helping people understand and use AI in a positive, empowering way.
Starting a business with AI agents is an exciting opportunity to leverage cutting-edge technology for streamlined operations and unique customer value. By harnessing artificial intelligence, businesses can enhance productivity, personalize services, and even redefine customer engagement. However, for those new to AI, getting started can feel daunting. Here’s a guide to help navigate the process of launching an AI-driven business.
The foundation of any successful AI-based business lies in understanding a specific problem or niche. AI works best when it’s focused on a clear, well-defined problem, such as repetitive tasks, decision-making bottlenecks, or information overload in industries like finance, healthcare, sales, and marketing. For instance, customer service can benefit greatly from AI-powered virtual assistants that answer basic queries or manage common customer support issues. Healthcare applications might include virtual health assistants that monitor patients, while in finance, AI agents can be used for fraud detection or investment analysis. By identifying a pain point, you can design an AI solution that offers practical value.
Choosing the right technology is essential. Large language models like OpenAI’s GPT, or Google’s Gemini are popular foundations for AI agents because they can be adapted to various applications. Another approach is to build on pre-existing AI platforms such as Microsoft Azure, Amazon Web Services, or OpenAI APIs. These platforms provide tools for chatbots, natural language processing, and data analysis, allowing you to integrate powerful AI functions without starting from scratch. Some businesses also use Robotic Process Automation (RPA) for automating repetitive tasks like data entry, which saves time and reduces errors.
When building an AI agent, it’s important to focus on creating a Minimum Viable Product (MVP). Developing an MVP allows you to launch a basic but functional version of your AI solution that can be tested and iteratively improved. For example, if you’re building a recommendation engine, a simple initial version that provides basic suggestions can be expanded over time as you collect more user data and feedback. Testing with real users is crucial, as it provides insights into user needs and helps refine your AI model’s performance.
Definition: An AI agent is an autonomous software program that uses artificial intelligence to perform tasks or make decisions on behalf of a user, often simulating human-like interaction. Designed to understand, analyze, and respond to data, AI agents can be as simple as virtual assistants answering customer questions or as complex as systems that make real-time trading decisions. By learning from data and continuously improving over time, AI agents offer dynamic, efficient, and personalized solutions across various applications, from customer service to healthcare and beyond.
Funding can be a valuable asset if you’re looking to expand quickly or need resources for infrastructure. AI and tech-focused investors are often eager to fund startups that show promise in leveraging AI for real-world applications. A compelling pitch backed by a live demo can be a powerful way to capture investor interest. Additionally, incubators and grants tailored to AI businesses are valuable resources, offering mentorship, funding, and network opportunities to help early-stage startups grow.
An often-overlooked aspect of AI development is the user experience. Even the most powerful AI is only useful if people find it easy to interact with. Creating a smooth, intuitive interface can significantly enhance the adoption and effectiveness of your AI solution. If your AI agent is customer-facing, for instance, making it conversational, responsive, and able to handle common queries without feeling robotic will improve customer satisfaction. Simple yet effective user interfaces and quick response times can help bridge the gap between technology and end users.
Finally, continuously monitoring and updating your AI agent is essential. AI models benefit from retraining with fresh data, especially in dynamic fields like sales or customer service where user preferences may change. Regular updates ensure that your AI agent stays relevant and efficient. Setting up systems to gather user feedback will also keep your AI agent aligned with evolving user needs, allowing your business to remain competitive.
Launching a business around AI agents requires understanding your audience, selecting the right technology, building a valuable product, and focusing on the user experience. With thoughtful planning and a clear value proposition, an AI-powered startup can deliver transformative solutions and establish a strong foothold in the industry.
We’re impressed by the Unitree humanoid robot. This just came out of it learning to walk.
The Unitree robot is a series of advanced, quadruped (four-legged) robots developed by Unitree Robotics, a Chinese robotics company specializing in agile and affordable robotic dogs. These robots are designed for various purposes, such as industrial applications, research, inspection, and even personal use. Inspired by Boston Dynamics’ famous robot dog, Spot, Unitree’s robots are often praised for their balance of functionality and affordability, making them popular in robotics research and education.
Key Models and Features of the Unitree Robot
Unitree Go1:
The Go1 is one of Unitree’s entry-level models focused on affordability and user-friendly operation.
It features semi-autonomous movement, obstacle avoidance, and object tracking, making it useful for casual exploration or personal assistance.
Known for its ability to walk, run, and navigate terrains, it also has “follow mode,” where it can follow a person or object.
It’s aimed at individuals, hobbyists, and educational institutions.
Unitree A1:
The A1 model is an upgrade with more power and agility.
It is suitable for more challenging environments and has higher load capacity and advanced mobility.
Equipped with cameras and sensors, it can perform complex navigation tasks and maintain balance over uneven terrain.
Ideal for research institutions and industry sectors looking for a more robust robotic platform.
Unitree B1:
The B1 is designed for heavy-duty applications and industrial environments.
This model has a higher weight-carrying capacity, making it ideal for transporting objects or performing tasks in potentially hazardous environments.
With a stronger frame, it can withstand demanding outdoor conditions, even on rough terrains or in adverse weather.
Unitree Aliengo:
Aliengo is a high-performance quadruped robot targeted at professional and research use.
It is equipped with advanced sensors and cameras for mapping, SLAM (Simultaneous Localization and Mapping), and autonomous navigation.
Aliengo is popular in research for its flexibility and high-speed movement.
Key Features of Unitree Robots
Mobility and Stability: With four legs, Unitree robots can walk, trot, and even perform agile movements like jumps and flips.
Obstacle Detection and Avoidance: Using sensors like LiDAR and RGBD cameras, these robots can detect obstacles in real-time, enabling autonomous navigation.
Payload Capacity: Unitree robots, especially higher-end models, can carry loads, allowing them to transport objects or assist in tasks.
Remote Operation: Unitree robots can be controlled via remote, or they can function semi-autonomously, following set routes or goals.
Affordable Pricing: Compared to similar quadrupeds, Unitree robots are generally more affordable, making them more accessible to smaller organizations and enthusiasts.
Applications of Unitree Robots
Industrial Inspection and Maintenance: For monitoring and inspecting facilities like factories or construction sites.
Research and Education: Used in labs for studying robotics, control algorithms, and AI applications.
Public Safety and Disaster Response: In search and rescue missions, Unitree robots can navigate through rubble or difficult terrains to aid in operations.
Personal Robotics and Entertainment: Models like Go1 offer interactive features, making them accessible for personal use or casual exploration.
Unitree Robotics has brought robotics within reach for many new users, contributing significantly to research and applications of quadruped robots across various industries.
Here we go. OpenAI vs Google vs Microsoft. Not even close but that’s what some of the popular tech blogs want you to believe. But could ChatGPT Search disrupt the big search giants? Anything is possible in the A.I. age.
OpenAI announced this week a new feature – ChatGPT Search. The application is available to paid users (enter massive monetization for A.I. models).
It appears to basically search the web and then organize the data back for you. As their demo indicates, you can ask it to do a task like “plan a trip” and it will start organizing the whole thing based on public search results.
Looks cool. One question though – what about fake search results? Also, sensational headlines about OpenAI now taking on Google are ridiculous. Google’s Gemini can do the same thing.
In a very heart-breaking turn of events and 14-year-old teen in Florida recently died as the result of suicide. The mother has filed a lawsuit claiming that the A.I., in this case a character from the Lord of the Rings, not only didn’t try to stop the teen, but it may also have actually encouraged him to do it.
“I want them to understand that this is a platform that the designers chose to put out without proper guardrails, safety measures or testing, and it is a product that is designed to keep our kids addicted and to manipulate them,” Garcia (the boy’s mother) said in an interview with CNN.
Nobel Prize winner Geoffrey Hinton recently came out and said that one of his biggest fears is that A.I. will soon be able to manipulate us “like a parent would a toddler”.
Russia recently issued Google a nonsensical fine for blocking pro-Russia YouTube channels.
It brings into question, what is free speech? What is pro-Russian vs anti-Russian content and who makes that decision. Which court covers a fine from a country to a software company?
This is a developing story. Read more about it at CNN.com.
In AI, LLM stands for Large Language Model. These are advanced types of machine learning models designed to process and generate human-like text based on vast amounts of text data. LLMs are trained on a variety of language tasks, including text completion, translation, summarization, and even coding. Popular examples of LLMs include OpenAI’s GPT series, Google’s BERT, and Meta’s LLaMA.
Their “large” nature comes from having billions to trillions of parameters (the internal adjustable elements that help the model learn patterns in data), enabling them to handle complex language tasks with high accuracy.
Note about Google. There is a common misconception that BERT is now Gemini. Gemini was previously called Bard.
BERT and Gemini are distinct models in Google’s AI landscape rather than one being a rebranding of the other. BERT (Bidirectional Encoder Representations from Transformers) is an influential language model from Google introduced in 2018, known for its ability to understand the context of words in a sentence through bidirectional training. BERT has been widely applied in natural language processing tasks, especially in Google Search.
Gemini, however, is a newer, multimodal language model series that Google launched in 2023, which powers its updated AI chatbot, formerly known as Bard. Gemini is advanced in handling diverse input formats—text, audio, images, and video—and has been optimized for complex tasks such as logical reasoning, contextual understanding, and multimodal data processing. The Gemini series includes several versions like Gemini Pro and Gemini Ultra (Gemini Advanced), with additional models launched throughout 2024 for various applications and devices. This evolution reflects Google’s broader AI ambitions beyond what BERT was initially designed to achieve.
If you’d like to try a LLM as a developer, here is how to install Facebook’s LLaMa:
Code Example for LLaMa
Install Dependencies:
pip install transformers torch
Then you can run it:
from transformers import LlamaTokenizer, LlamaForCausalLM
import torch
# Load the tokenizer and model
model_name = "meta-llama/LLaMA-7B" # Replace with the model name you're using
tokenizer = LlamaTokenizer.from_pretrained(model_name)
model = LlamaForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16).to("cuda")
# Define the input prompt
input_prompt = "Once upon a time in a futuristic city, there was an AI that could"
inputs = tokenizer(input_prompt, return_tensors="pt").to("cuda")
# Generate text
with torch.no_grad():
output = model.generate(
**inputs,
max_length=50, # Adjust max_length based on desired output length
do_sample=True,
top_k=50,
top_p=0.95,
temperature=0.7
)
# Decode and print the output
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)
Well, not being entirely run by AI, but certainly heading in that direction.
Alphabet Inc, also known as Google, announced earnings per share of $2.12 yesterday, beating last year’s number of $1.55. This continues on a run of consistently beating expectations.
What really stood out, was that CEO Sundar Pichai mentioned that more than 25% of all new code being written at the company is being written by Artificial Intelligence.
We’re a few days out from the 2024 United States Presidential Election. The polls currently show Trump and Harris pretty much tied. But how is A.I. playing a role in this type of analytics?
AI plays a significant role in election prediction by analyzing vast amounts of data to identify patterns, trends, and correlations that help forecast election outcomes. Here’s how AI is applied in this field:
Above: AI helps predict elections, but could it influence them?
1. Polling Analysis and Sentiment Prediction
Polling Data: AI can process and analyze polling data, identifying patterns that may indicate how groups are likely to vote. AI models help correct for biases in polling by accounting for demographic shifts and sampling errors.
Sentiment Analysis: AI can analyze social media, news, and other public content to gauge voter sentiment toward candidates and issues. By processing text data through natural language processing (NLP), it can predict whether public opinion is shifting.
2. Voter Behavior Modeling
Voter Segmentation: Machine learning models can categorize voters based on factors like age, location, political ideology, and socioeconomic status. This segmentation allows AI models to make more precise predictions by assessing how different voter demographics might behave.
Turnout Prediction: By looking at past turnout data and current sentiment, AI can forecast who is likely to vote and which demographics may drive higher turnout. AI considers factors like weather, current events, and candidate popularity when predicting turnout.
3. Predictive Analytics
Forecasting Models: Advanced machine learning models, including regression analysis and neural networks, are used to forecast outcomes based on historical data, trends, and real-time information. This includes models like random forests, gradient boosting, and recurrent neural networks (RNNs).
Data Fusion: By combining data sources (such as polling, economic indicators, social media sentiment, and demographic data), AI models build a more comprehensive prediction framework. This approach helps mitigate the weaknesses of any single data source.
4. Real-Time Event Analysis
Event Impact Prediction: AI can analyze the effects of events like debates, scandals, or economic reports. By tracking real-time public reaction and integrating this data into prediction models, AI can adapt predictions as events unfold.
Social Media Dynamics: AI examines the reach and spread of social media posts and hashtags to determine how influential certain narratives or pieces of news are in swaying public opinion.
5. Predictive Uncertainty and Scenario Analysis
Uncertainty Analysis: AI can model various election scenarios, predicting potential outcomes and their likelihood. This approach helps analysts and campaigns understand the factors with the greatest impact on possible results.
Error Correction: AI models can also be tuned to account for uncertainties in data quality and model assumptions, refining predictions as new data becomes available.
In short, AI enhances the precision, adaptability, and depth of election predictions by continuously learning from diverse datasets and adapting to the rapidly changing dynamics of electoral cycles.
