But what comes next?

Workforce

The next big shift: Every company on the planet will start replacing workloads with AGI wherever possible to maximize profit margins. Companies won’t hire as much because their existing employees will be 10x more productive with AI agents.

Startups and New Companies

Launching new startups — many of which are essentially databases with a layer of applications — will become nearly impossible. Why? Because SaaS is much more than just a product; it involves extensive behind-the-scenes deals, massive distribution networks, and an established customer base. Existing SaaS giants will dominate every corner of the industry, leveraging AGI to achieve 100x more efficient processes. New players will struggle to compete. Founders traditionally find opportunities in dysfunctional parts of the industry or niche markets, but with AGI, even those opportunities will vanish.

Healthcare

Diagnostic medicine will be one of the first sectors disrupted by AGI. Initially, there will be talks about fairness, democratizing healthcare, and empowering doctors by “keeping humans in the loop.” However, over time, the economic pressure and efficiency of AGI will reduce the role of doctors, eventually replacing them with AGI and healthcare robotics. The sector’s current shortages and inefficiencies will accelerate this transformation.

Humanity’s Transformation

Every aspect of humanity will change — not solely because of OpenAI, xAI, Google DeepMind, or others — but because AGI can be built by anyone with enough incentives and the resources to purchase GPUs. And with GPU costs dropping over time, AGI development will become increasingly accessible.

So, What’s the Right Move?

There will be countless positive aspects to the emergence of AGI, but you need to make the right decisions now! Keep investing in companies, particularly tech companies, because they will soon reshape the entire world.

..what do you think?

AGI in 2025 |Do you think what matters today will still matter in the coming months? TL;DR: No! was originally published in Becoming Human: Artificial Intelligence Magazine on Medium, where people are continuing the conversation by highlighting and responding to this story.

Tomorrow, September 24, 2024, San Francisco will host one of the biggest global AI events of the year: the Chatbot Conference! Whether you’re passionate about artificial intelligence, curious about chatbots, or simply eager to connect with industry leaders, this conference is for you.

Why You Should Attend

This is more than just a conference; it’s your opportunity to explore how AI is transforming industries around the world. Here’s what you can look forward to:

• Inspiring Talks: Hear from AI innovators leading the way in technology and business.
• Interactive Workshops: Roll up your sleeves and create AI solutions that are ready for the real world.
• Networking Opportunities: Meet like-minded professionals, tech enthusiasts, and thought leaders who are driving the AI conversation.

What’s on the Agenda?

The event will feature everything from cutting-edge chatbot demos to hands-on AI development workshops. Discover how AI agents are evolving, and learn best practices from seasoned professionals. You’ll walk away with actionable insights and new connections.

Explore the full agenda at the Chatbot Conference.

Act Now, Don’t Miss Out!

This is your chance to take part in an event that will define the future of AI and chatbot technology.

See you there! Together, let’s learn, collaborate, and be inspired.

Be Part of the AI Revolution at the Chatbot Conference Tomorrow! was originally published in Becoming Human: Artificial Intelligence Magazine on Medium, where people are continuing the conversation by highlighting and responding to this story.

The conference features a range of events designed to enrich attendees’ understanding of the chatbot industry:

• Expert Keynotes: Get insights from industry leaders at the forefront of AI innovation.
• Workshops: These certified workshops provide hands-on experience, helping participants design and deploy their own AI agents. Whether you’re looking to master AI automation or enhance chatbot designs, these workshops are crafted to guide you through each step​.
• Panels and Discussions: Interactive panels allow you to learn directly from pioneers in the field, addressing topics like AI ethics, automation, and cognitive frameworks.

💬 Why Attend?

If you’re eager to stay ahead of the curve, the Chatbot Conference offers a unique opportunity to:

• Learn from the Best: Access cutting-edge knowledge from top AI developers and researchers.
• Network with Peers: Engage with a vibrant community of innovators, entrepreneurs, and AI professionals.
• Discover the Future of AI: From exploring AI multi-agent systems to gaining insight into practical implementations of chatbots in business, the event provides a glimpse into the next wave of conversational technology​.

🔍 Looking Ahead

With upcoming events already lined up, now is the time to get involved. Whether you’re looking to deepen your technical skills or understand how AI can revolutionize your business, the Chatbot Conference offers a unique and valuable platform to learn and grow.

Don’t miss your chance to be part of the conversation shaping the future of conversational AI!

Join the Most-Awaited Chatbot Conference was originally published in Becoming Human: Artificial Intelligence Magazine on Medium, where people are continuing the conversation by highlighting and responding to this story.

Exciting news ahead! With an incredible surge of enthusiasm, we're rolling out an exclusive Online Only option for this year's Chatbot Conference, kicking things off with an absolutely phenomenal launch!

Today kicks off an incredible flash sale, showcasing a limited selection of tickets—only 18 passes available at this unbeatable price.

🚀 Get the Best Deal!

• Prepare to grab this incredible opportunity with a massive 50% off your exclusive Online Only Virtual Pass!
• Exclusive Offer: Take advantage of a fantastic 30% discount on all in-person ticket options!
• Limited Opportunity: Just 18 Tickets Up for Grabs at This Rate

Grab this chance to plunge into the innovative realm of AI and chatbot technology without emptying your wallet.

Secure your spot now and join an exhilarating community of trailblazers in the AI arena!

See you there!

🚀 Limited Time Offer: Get Your Exclusive Online Passes to the Chatbot Conference — Act Fast! 🚀 was originally published in Becoming Human: Artificial Intelligence Magazine on Medium, where people are continuing the conversation by highlighting and responding to this story.

Water industry professionals explored the intersection of artificial intelligence (AI) and machine learning (ML) during a pre-conference workshop in Ocean City, Maryland yesterday, discovering that while AI’s roots go back to 1948, today’s Generative AI has the potential to completely upend their industry.

Designed to make AI technologies accessible and relevant, the sessions emphasized the critical role of data and the importance of data governance, sparking excitement and curiosity among participants — all leading up to the Chesapeake Tri-Association Conference (TriCon), the water industry’s premier event.

Professionals from the City of Rockville, WSSC, City of Baltimore, DC Water, and regional engineering companies gathered to explore how AI can be effectively applied to their field. Presented by the CWEA and CSAWWA Asset Management Committee, the session featured Craig Daly from the City of Baltimore, Travis Wagner of Trinnex, Brandon O’Daniel of Xylem, John Smith of Haley Ward and Davar Ardalan of TulipAI.

The workshop focused on practical, actionable steps, showing participants how these tools can enhance accuracy, save time, and optimize their water systems. Breakout sessions also introduced participants to the real-world applications of generative AI tools.

John Smith of Haley Ward and Davar Ardalan of TulipAI led a special segment titled “Responsible AI Adventures: Innovating Environmental Engineering,” which highlighted the importance of ethical considerations when using AI in the water industry.

Smith and Ardalan introduced the beta version of John Smith GPT, an AI assistant designed to support John and his team of environmental engineers in tasks like proposal writing, cost estimating, and marketing strategies. They emphasized two critical points: first, never share proprietary information with an open AI tool; and second, always be transparent when using AI, just as you would with a bibliography or by naming your sources. This transparency is essential for maintaining trust and integrity in how AI is integrated into professional practices.

Try the beta version of John Smith GPT here. The custom AI:

Leverages decades of civil engineering knowledge from veteran civil engineer John Oliver Smith.

Provides information on materials relevant to grant projects, enhancing proposal detail.

Supplies data on eco-friendly materials and methods, supporting sustainability objectives.

John Smith also underscored the importance of not sharing confidential information with AI systems. He likened AI to a powerful tool that, like any other, needs to be used responsibly. Moreover, he encouraged attendees to pilot AI tools with their teams before full-scale implementation, allowing for collaborative input and refinement of the technology to suit specific needs. Their message was clear: AI can transform the industry, but it must be used thoughtfully and with full awareness of its ethical implications.

As AI tools continue to develop, sessions like this at TriCon are essential for staying informed and prepared. They equip water professionals with the tools and understanding they need to harness new technologies effectively and responsibly.

This content was crafted with the assistance of artificial intelligence, which contributed to structuring the narrative, ensuring grammatical accuracy, summarizing key points, and enhancing the readability and coherence of the material.

Related Story:

Hard Hats and High Tech: Bringing AI and Engineering Innovation to Miami

Demystifying AI in the Water Industry was originally published in Becoming Human: Artificial Intelligence Magazine on Medium, where people are continuing the conversation by highlighting and responding to this story.

Part 1: on face embeddings and how to run face search on the fly

In this entry (Part 1) we’ll introduce the basic concepts for face recognition and search, and implement a basic working solution purely in Python. At the end of the article you will be able to run arbitrary face search on the fly, locally on your own images.

In Part 2 we’ll scale the learning of Part 1, by using a vector database to optimize interfacing and querying.

Face matching, embeddings and similarity metrics.

The goal: find all instances of a given query face within a pool of images.
Instead of limiting the search to exact matches only, we can relax the criteria by sorting results based on similarity. The higher the similarity score, the more likely the result to be a match. We can then pick only the top N results or filter by those with a similarity score above a certain threshold.

To sort results, we need a similarity score for each pair of faces <Q, T> (where Q is the query face and T is the target face). While a basic approach might involve a pixel-by-pixel comparison of cropped face images, a more powerful and effective method uses embeddings.

An embedding is a learned representation of some input in the form of a list of real-value numbers (a N-dimensional vector). This vector should capture the most essential features of the input, while ignoring superfluous aspect; an embedding is a distilled and compacted representation.
Machine-learning models are trained to learn such representations and can then generate embeddings for newly seen inputs. Quality and usefulness of embeddings for a use-case hinge on the quality of the embedding model, and the criteria used to train it.

In our case, we want a model that has been trained to maximize face identity matching: photos of the same person should match and have very close representations, while the more faces identities differ, the more different (or distant) the related embeddings should be. We want irrelevant details such as lighting, face orientation, face expression to be ignored.

Once we have embeddings, we can compare them using well-known distance metrics like cosine similarity or Euclidean distance. These metrics measure how “close” two vectors are in the vector space. If the vector space is well structured (i.e., the embedding model is effective), this will be equivalent to know how similar two faces are. With this we can then sort all results and select the most likely matches.

Implement and Run Face Search

Let’s jump on the implementation of our local face search. As a requirement you will need a Python environment (version ≥3.10) and a basic understanding on the Python language.

For our use-case we will also rely on the popular Insightface library, which on top of many face-related utilities, also offers face embeddings (aka recognition) models. This library choice is just to simplify the process, as it takes care of downloading, initializing and running the necessary models. You can also go directly for the provided ONNX models, for which you’ll have to write some boilerplate/wrapper code.

First step is to install the required libraries (we advise to use a virtual environment).

pip install numpy==1.26.4 pillow==10.4.0 insightface==0.7.3

The following is the script you can use to run a face search. We commented all relevant bits. It can be run in the command-line by passing the required arguments. For example

 python run_face_search.py -q "./query.png" -t "./face_search"

The query arg should point to the image containing the query face, while the target arg should point to the directory containing the images to search from. Additionally, you can control the similarity-threshold to account for a match, and the minimum resolution required for a face to be considered.

The script loads the query face, computes its embedding and then proceeds to load all images in the target directory and compute embeddings for all found faces. Cosine similarity is then used to compare each found face with the query face. A match is recorded if the similarity score is greater than the provided threshold. At the end the list of matches is printed, each with the original image path, the similarity score and the location of the face in the image (that is, the face bounding box coordinates). You can edit this script to process such output as needed.

Similarity values (and so the threshold) will be very dependent on the embeddings used and nature of the data. In our case, for example, many correct matches can be found around the 0.5 similarity value. One will always need to compromise between precision (match returned are correct; increases with higher threshold) and recall (all expected matches are returned; increases with lower threshold).

What’s Next?

And that’s it! That’s all you need to run a basic face search locally. It is quite accurate, and can be run on the fly, but it doesn’t provide optimal performances. Searching from a large set of images will be slow and, more important, all embeddings will be recomputed for every query. In the next post we will improve on this setup and scale the approach by using a vector database.

Want to Connect?
You can catch a glimpse of my latest experiments and explanations on Twitter or Threads and see my graphics results on Instagram.

Building a Local Face Search Engine — A Step by Step Guide was originally published in Becoming Human: Artificial Intelligence Magazine on Medium, where people are continuing the conversation by highlighting and responding to this story.

For the next two weeks, you can save up to $400 on Tickets to the Chatbot Conference 2024.

Whether you’re a returning attendee or new to our community, this is the perfect chance to experience the future of AI and chatbot technology at a discounted rate.

Here’s What You Can Expect:

• Insightful Keynotes: Hear from pioneers and innovators who are shaping the world of conversational AI.
• Interactive Workshops: Dive into hands-on sessions where you’ll learn to design and build AI-Agent in 48hrs and earn a Certification.
• Networking Opportunities: Connect with top industry leaders and peers in the San Francisco Bay Area and from around the globe.

If you want to learn more about the conference you see the Agenda, Testimonials and Workshops here.

How to Redeem Your Discount

Simply go to the website and select the ticket marked SALE. But hurry, this offer is available for a limited time only!

🔖 Sale Details:

• Discount Offered: Enjoy $200 Off on Day 1 Passes & $400 Off on all 3 Days of the Chatbot Conference
• Sale Duration: Now through August 30th — don’t miss out!

Don’t miss out on this opportunity to save on your ticket and join us for an unforgettable experience.

We promise you a conference filled with learning, innovation, and plenty of networking opportunities.

Secure your spot today and be part of the future of AI and chatbot technology. We can’t wait to see you there!

Best regards,

Chatbot Conference Team

P.S. Have any questions? Feel free to reach out to us at team@chatbotconference.com or via chat. We’re here to help!

Save up to $400 on Your Conference Tickets! was originally published in Becoming Human: Artificial Intelligence Magazine on Medium, where people are continuing the conversation by highlighting and responding to this story.

As the tech world buzzes with the unveiling of Apple Intelligence, expectations are soaring. The leap from iPhone to AI-Phone paints a picture of a future where our devices aren’t just tools but partners capable of anticipating our needs and actions. Yet, amidst this enthusiastic anticipation, it’s crucial to examine the potential pitfalls that might cause Apple Intelligence to fall short of these lofty expectations.

Technological Overreach?

The very ambition that makes Apple Intelligence seem revolutionary could also be its Achilles’ heel. Apple plans to seamlessly integrate advanced AI across its suite of devices, promising an ecosystem where your iPad, iPhone, and Mac work together more intelligently than ever. However, the complexity of implementing such deep, cross-platform integration without glitches, privacy issues, or user frustration is immense. Could Apple be promising more than current technology realistically allows?

The Privacy Paradox

Apple has long championed privacy as a cornerstone of its brand, yet the increased data processing required by Apple Intelligence could strain this commitment. With features like real-time language and image processing touted, the volume of data analyzed by AI will be vast. Even with promises of on-device processing, the potential for privacy breaches grows as more personal information is constantly analyzed by AI. Will Apple manage to uphold its privacy standards, or will the allure of AI functionality tempt it to compromise?

User Experience and Learning Curve

Another potential shortfall could be in the user experience. Apple Intelligence introduces a slew of advanced functionalities — from smarter Siri interactions to complex image editing tools. While these advancements are impressive, they also introduce a steep learning curve. Not all users are ready for such sophistication from their devices. The risk? Apple could alienate users who prefer simplicity and reliability over cutting-edge features.

Potential for Disruption vs. Practical Utility

Another area where Apple Intelligence could disappoint is the gap between potential and utility. The tech showcased, like using AI for predictive text or advanced photo editing, is undoubtedly forward-thinking. However, if these features don’t translate into tangible improvements in daily use or if they come off as gimmicky rather than genuinely useful, user adoption may lag. Apple’s challenge is to ensure that Apple Intelligence feels essential, not just impressive.

The Compatibility Conundrum

Lastly, Apple Intelligence will initially only be available on the latest devices equipped with the newest chips. This limitation means that a significant portion of Apple’s user base won’t have immediate access to these features. Limiting cutting-edge capabilities to high-end models could frustrate users who are unable or unwilling to upgrade, potentially slowing down widespread adoption.

Conclusion: A Cautious Approach

As we edge into the AI-Phone era, it’s evident that Apple Intelligence could significantly shift how we interact with our devices. Yet, this new frontier is fraught with challenges that could hinder Apple’s vision from fully materializing. Users, analysts, and enthusiasts should temper their expectations with a healthy dose of skepticism, focusing on the practical implementation rather than the promised revolution.

Check out our full analysis here for more information about Apple Intelligence and how it will integrate generative AI into the iPhone, iPad, and Mac.

Why Apple Intelligence Might Fall Short of Expectations? was originally published in Becoming Human: Artificial Intelligence Magazine on Medium, where people are continuing the conversation by highlighting and responding to this story.