#AI Developers

[justdavina]

@justdavina of San Francisco Pride Collection 2025

[siriuslyobsessedwithfiction]

Who decided it was a good idea to make a "if you don't know the answer, just lie" feature in ChatGPT and other AI chatbots? When you question them, they just give you another inaccurate answer. I had to literally tell ChatGPT that it's okay if it doesn't know the answer, it can just tell me so. I rarely, and I mean rarely, use AI and it makes me more and more uncomfortable every time.

[thursdayisbetterthanfriday]

Simulations of Cybernetic Meadows - Recreating Life

Part 1 - History of Technology

The New Yorker recently put out a great article, covering the modern day conversations we're having about how "lifelike" AI systems are:

It's a great article, which covers a lot of modern discourse around the last 25 years around how we've attempted to make "lifelike" machines. In an age of incredible speed and velocity in progress, we often forget how things have ended up this way. If you look, it turns out the quest for lifelike machines has been going on for a lot longer than people might suppose.

1st Century Antiquity - Age of Inquiry - The Mortal Hephaestus:

When people think of lifelike robots, they often jump to the robots of the 20th century. The past, however, is deep and full of secrets for those who know where to look.

Hero of Alexandria was a master craftsman, and mathematician, famous for one of the world's first steam engines, and Heron's formula for calculating the area of a triangle. He also created some of the first known humanoid automata.

This video is slightly misleading, as it shows a combination of his efforts, and that of another, but Hero himself was able to create moving automata that used weights as potential energy to drive wheeled devices around a room. The Herakleidon Museum does also have videos recreating his original mechanism.

The Greeks were, in particular, prolific in their creation of automata which mimicked life.

18th Century - Artisanal Age - Dreams of Automata

The writer is a mechanical automaton, created in 1770, by the aritst Pierre Jacquet-Droz. It is a marvel of early engineering, using a series of what are called "cams" to direct a machine in the shape of a child to write sentences.

It combines the mechanical aspects of writing, with the aesthetic appearance of a child, in order to create a work of art. It is also one of the first programmable machines, using a series of replacable letters to change what words are written.

But machines with lifelike qualities are not just limited to human motions. Master British automata makers John Joseph Merlin and John Cox created an astoundingly lifelike rendition of a swan in 1773, which preened its feathers, and caught fish from a pond.

Both of these devices were driven by the creation of advanced clockwork, and machining technologies. As with today's semiconductor revolution, clockwork started off the size of rooms until it was progressively miniaturized into the palm of your hand.

While the 18th century artisans and mechanists were able to create wondrous mechanical motions, they were not truly able to replicate the mechanics of "thinking". The writer is one of the few examples of programmatic "thinking", but cannot independently operate.

20th Century - The Cybernetic Age - Adaptation and Evolution:

The advent of thermodynamics, and the rise of electronics, led to new means by which to create "living machines". In the 20th century, it also led to differing approaches to simulating life, such as cybernetics and expert systems. Walter Grey's tortoise robots are a great example of the cybernetic attempts to create complex behaviours by using simple rules in the form of both a light and touch sensor.

This robotic tortoise was able to exhibit very lifelike behaviours and reach goals without explicit instructed programming, such as with The Writer automaton.

This is a key "adaptation" and evolution from the concept of an automaton. Whereas automatons had simulated the mechanics of life though motion and muscle, robots such as the tortoise started to simulate the mechanics of thought through electronic wiring and circuitry.

The rise of the integrated circuit, and transistor, has been key to allowing modern automata/robots to reach advanced levels of ability unthinkable in pre-modern times.

Shakey represents another branch of 20th century robotics, the expert system, led by ideas similar to the 19th century automata, but updated exponentially. By using several sensors, Shakey was able to navigate around rooms and create internal representations of the rooms it was in, in order to perform objectives.

21st Century - Age of Artificial Neural Networks - Memory and Thought:

Similar to the age of automata, we have entered another age of biomimicry, this time using Artificial Neural Networks. Instead of attempting to replicate the mechanics of actions, we are instead attempting to replicate the organic mechanics of thought. One of the key ideas behind life is that it is self-sustaining - it operates all on its own. 18th/19th century automata weren't able to do this and 20th century automata/robotics did to a certain extent, but were often still viewed as machines with function.

Sony's AIBO represents a more "lifelike" kind of design - where the entire system is driven by "curiosity" rather than by specific instruction. While it posseses some subroutines, similar to its 20th century counterparts, it surpasses them in how it can also adaptively learn new behaviours to better suit its surroundings, both in practical settings (finding its way around) and social settings (recognising people, and interacting with them in a socially "successful" way). The fields of reinforcement learning and artificial neural networks allow for complex behaviours to be simulated for the first time. Sony's AIBO is an excellent example of this in practice, with the robotic dog able to learn through positive reinforcement verbally, or via tactile methods, to promote certain behaviours.

It can even use computer vision capabilities to recognise specific users on sight, as well as allowing it to create its own internal model of the world around it. Much like the 18th to 19th century automata, AIBO also attempts to replicate the actions of life itself, with its design, and actions, replicating that of a puppy or small dog. Unlike the automatons of old, however, the use of tactile, vision and sound sensors allow its form to play some function in its operation aside from aesthetics.

Cybernetic Meadows - The Future?

As we endow robotics systems with ever greater ability, how will we interact with them in the future, and how lifelike will they truly become? Humanity has always, in some form or another, sought to replicate life itself through the medium of art and engineering. Only in time will we see the results.

[smitpatel1420]

List of most reputed artificial intelligence consulting firms

Discover the best AI consulting firms for 2024 known for their outstanding AI solutions, creative methods, and satisfied clients. These companies are leaders in the field, offering a wide range of services to assist companies in utilising AI to increase productivity and growth.

Businesses are looking to top AI consulting organisations for their expertise as the use of artificial intelligence grows. With a predicted growth rate of 37.3%, the AI market, which was valued at $207 billion in 2023, is expected to reach $1,394 billion by 2029. The top 10 reputable AI consulting firms for 2024 are listed below; they are praised for their inventiveness, excellence, and satisfied clients.

To fully utilise artificial intelligence, choosing the correct AI consulting firm is crucial. The businesses on the above list are leaders in their respective fields and each has special skills and advantages. By collaborating with these leading AI consulting companies, companies may successfully incorporate AI technologies and meet their objectives for digital transformation.

[technon899]

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[aiupdates]

🌐 Welcome to the forefront of artificial intelligence innovation AdeptAI

Progressive ML research and product lab, committed to shaping the future of general intelligence.

[redbixbite-solutions]

Everything You Need to Know About Machine Learning

Ready to step into the world of possibilities with machine learning? Learn all about machine learning and its cutting-edge technology. From what do you need to learn before using it to where it is applicable and their types, join us as we reveal the secrets. Read along for everything you need to know about Machine Learning!

What is Machine Learning?

Machine Learning is a field of study within artificial intelligence (AI) that concentrates on creating algorithms and models which enable computers to learn from data and make predictions or decisions without being explicitly programmed. The process involves training a computer system using copious amounts of data to identify patterns, extract valuable information, and make precise predictions or decisions.

Fundamentally, machine Learning relies on statistical techniques and algorithms to analyze data and discover patterns or connections. These algorithms utilize mathematical models to process and interpret data. Revealing significant insights that can be utilized across various applications by different AI ML services.

What do you need to know for Machine Learning?

You can explore the exciting world of machine learning without being an expert mathematician or computer scientist. However, a  basic understanding of statistics, programming, and data manipulation will benefit you. Machine learning involves exploring patterns in data, making predictions, and automating tasks.

 It has the potential to revolutionize industries. Moreover, it can improve healthcare and enhance our daily lives. Whether you are a beginner or a seasoned professional embracing machine learning can unlock numerous opportunities and empower you to solve complex problems with intelligent algorithms.

Types of Machine Learning

Let’s learn all about machine learning and know about its types.

Supervised Learning

Supervise­d learning resemble­s having a wise mentor guiding you eve­ry step of the way. In this approach, a machine le­arning model is trained using labele­d data wherein the de­sired outcome is already known.

The­ model gains knowledge from the­se provided example­s and can accurately predict or classify new, unse­en data. It serves as a highly e­ffective tool for tasks such as dete­cting spam, analyzing sentiment, and recognizing image­s.

Unsupervised Learning

In the re­alm of unsupervised learning, machine­s are granted the autonomy to e­xplore and unveil patterns inde­pendently. This methodology mainly ope­rates with unlabeled data, whe­re models strive to une­arth concealed structures or re­lationships within the information.

It can be likene­d to solving a puzzle without prior knowledge of what the­ final image should depict. Unsupervise­d learning finds frequent application in dive­rse areas such as clustering, anomaly de­tection, and recommendation syste­ms.

Reinforcement Learning

Reinforce­ment learning draws inspiration from the way humans le­arn through trial and error. In this approach, a machine learning mode­l interacts with an environment and acquire­s knowledge to make de­cisions based on positive or negative­ feedback, refe­rred to as rewards.

It's akin to teaching a dog ne­w tricks by rewarding good behavior. Reinforce­ment learning finds exte­nsive applications in areas such as robotics, game playing, and autonomous ve­hicles.

Machine Learning Process

Now that the diffe­rent types of machine le­arning have been e­xplained, we can delve­ into understanding the encompassing proce­ss involved.

To begin with, one­ must gather and prepare the­ appropriate data. High-quality data is the foundation of any triumph in a machine le­arning project.

Afterward, one­ should proceed by sele­cting an appropriate algorithm or model that aligns with their spe­cific task and data type. It is worth noting that the market offe­rs a myriad of algorithms, each possessing unique stre­ngths and weaknesses.

Next, the machine goes through the training phase. The model learns from making adjustments to its internal parameters and labeled data. This helps in minimizing errors and improves its accuracy.

Evaluation of the machine’s performance is a significant step. It helps assess machines' ability to generalize new and unforeseen data. Different types of metrics are used for the assessment. It includes measuring accuracy, recall, precision, and other performance indicators.

The last step is to test the machine for real word scenario predictions and decision-making. This is where we get the result of our investment. It helps automate the process, make accurate forecasts, and offer valuable insights. Using the same way. RedBixbite offers solutions like DOCBrains, Orionzi, SmileeBrains, and E-Governance for industries like agriculture, manufacturing, banking and finance, healthcare, public sector and government, travel transportation and logistics, and retail and consumer goods.

Applications of Machine Learning

Do you want to know all about machine learning? Then you should know where it is applicable.

Natural Language Processing (NLP)- One are­a where machine le­arning significantly impacts is Natural Language Processing (NLP). It enables various applications like­ language translation, sentiment analysis, chatbots, and voice­ assistants. Using the prowess of machine le­arning, NLP systems can continuously learn and adapt to enhance­ their understanding of human language ove­r time.

Computer Vision- Computer Vision pre­sents an intriguing application of machine learning. It involve­s training computers to interpret and compre­hend visual information, encompassing images and vide­os. By utilizing machine learning algorithms, computers gain the­ capability to identify objects, faces, and ge­stures, resulting in the de­velopment of applications like facial re­cognition, object detection, and autonomous ve­hicles.

Recommendation Systems- Recomme­ndation systems have become­ an essential part of our eve­ryday lives, with machine learning playing a crucial role­ in their developme­nt. These systems care­fully analyze user prefe­rences, behaviors, and patte­rns to offer personalized re­commendations spanning various domains like movies, music, e­-commerce products, and news article­s.

Fraud Detection- Fraud dete­ction poses a critical concern for businesse­s. In this realm, machine learning has e­merged as a game-change­r. By meticulously analyzing vast amounts of data and swiftly detecting anomalie­s, machine learning models can ide­ntify fraudulent activities in real-time­.

Healthcare- Machine learning has also made great progress in the healthcare sector. It has helped doctors and healthcare professionals make precise and timely decisions by diagnosing diseases and predicting patient outcomes. Through the analysis of patient data, machine learning algorithms can detect patterns and anticipate possible health risks, ultimately resulting in early interventions and enhanced patient care.

In today's fast-paced te­chnological landscape, the field of artificial inte­lligence (AI) has eme­rged as a groundbreaking force, re­volutionizing various industries. As a specialized AI de­velopment company, our expe­rtise lies in machine le­arning—a subset of AI that entails creating syste­ms capable of learning and making predictions or de­cisions without explicit programming.

Machine learning's wide­spread applications across multiple domains have transforme­d businesses' operations and significantly e­nhanced overall efficie­ncy.

[vasundhara-infotech]

Top AI Tools for Developers in 2025

[softwarecompany909809]

Top Reasons Why AI Models Fail and How to Avoid Them

Many AI projects fail before they deliver real value. Find the most common reasons behind AI model failures, including poor data quality, overfitting, lack of domain expertise, and deployment challenges. It also provides actionable solutions and best practices for building reliable, scalable, and successful AI systems. Whether you're a developer, data scientist, or business leader, these insights will help you avoid critical mistakes and optimize your AI strategy for long-term impact.

[justdavina]

@justdavina of San Francisco AI Fashion 2025

[sid099]

Why You Should Hire Data Scientist to Drive Business Growth in 2025

In the era of big data, businesses are sitting on a goldmine of information. But without the right expertise, this data remains just numbers. That's where data scientists come in. If you want to stay competitive in 2025 and beyond, it's essential to hire data scientist to extract meaningful insights, improve processes, and make informed decisions.

What Does a Data Scientist Do?

Before you hire data scientist, it's important to understand their role. A data scientist is an analytical expert who uses advanced tools and techniques like machine learning, predictive modeling, and statistical analysis to interpret complex data.

Their work helps businesses:

Forecast trends and market behavior

Personalize customer experiences

Improve operational efficiency

Identify new revenue opportunities

By choosing to hire data scientist, companies unlock their data's full potential and gain a strategic advantage over their competitors.

Why Your Business Needs to Hire Data Scientist

Here are several compelling reasons why it's the right time to hire data scientist:

1. Turn Raw Data into Actionable Insights

Raw data is of no use if you can't analyze it effectively. A skilled data scientist can transform large volumes of structured and unstructured data into clear insights, helping you make smarter decisions.

2. Gain a Competitive Edge

When you hire data scientist, you empower your business to predict trends before they become obvious. This allows you to innovate faster and outperform competitors in your niche.

3. Improve Customer Retention

Understanding customer behavior is critical. Data scientists analyze purchasing patterns, feedback, and interaction history to create personalized experiences that boost loyalty and reduce churn.

4. Optimize Marketing Strategies

If your marketing ROI is stagnant, it's time to hire data scientist. They can measure campaign effectiveness, identify the best-performing channels, and provide data-backed recommendations to increase conversions.

When Is the Right Time to Hire Data Scientist?

Not every company needs a full-time data scientist from day one. But if your organization:

Collects a significant amount of customer or operational data

Needs to make high-stakes strategic decisions

Wants to integrate AI or automation

Lacks the internal expertise to manage data pipelines

...then it's the right time to hire data scientist.

Start by identifying your data goals. Whether it's optimizing supply chains, improving user experience, or detecting fraud—knowing your objective will guide your hiring process.

How to Hire Data Scientist for Your Business

Here's a simple 4-step process to hire data scientist efficiently:

1. Define the Role Clearly

Determine whether you need a generalist or a specialist. Will the data scientist work with marketing, product, or operations? Define clear KPIs and expectations before starting the hiring process.

2. Look for the Right Skills

When you hire data scientist, look for skills like:

Python, R, or SQL

Data visualization (Tableau, Power BI)

Machine learning

Statistical modeling

Communication and storytelling with data

3. Consider Cultural Fit

A great data scientist must work closely with different teams. Make sure the candidate can collaborate across departments and communicate insights in an understandable way.

4. Use Trusted Hiring Partners

To speed up the process and find top-tier talent, work with trusted hiring agencies like Magic Factory that specialize in helping businesses hire data scientist effectively.

Conclusion

Data is the new oil—but only if you have the right tools and experts to refine it. As your business continues to grow in the digital age, it's essential to hire data scientist who can turn data into your most valuable asset.

Whether you're looking to enhance customer experience, optimize internal operations, or create predictive models that drive innovation, hiring the right data expert is a game-changer.

At Magic Factory, we help organizations like yours hire data scientist who are not just technically strong but also aligned with your business goals. Reach out to us today to unlock the full power of your data with the right talent by your side.

[atcuality1]

From Code to Cognitive – Atcuality’s Intelligent Tech Framework

Atcuality is redefining enterprise success by delivering powerful, scalable, and intelligent software solutions. Our multidisciplinary teams combine cloud-native architectures, agile engineering, and business domain expertise to create value across your digital ecosystem. Positioned at the crossroads of business transformation and technical execution, artificial intelligence plays a vital role in our delivery model. We design AI algorithms, train custom models, and embed intelligence directly into products and processes, giving our clients the edge they need to lead. From automation in finance to real-time analytics in healthcare, Atcuality enables smarter decisions, deeper insights, and higher efficiency. We believe in creating human-centered technology that doesn’t just function, but thinks. Elevate your systems and your strategy — with Atcuality as your trusted technology partner.

[samsmithspk]

Building an AI Center of Excellence: Steps to Hire AI Developer Talent

Beginning an AI Center of Excellence begins with having the capability to hire AI developer talent that aligns with your strategic direction. This includes ensuring ongoing innovation, governance, and scalable deployment of AI throughout your organization.

[impact-newswire]

Zindi Supports Cassava Technologies with 85 K Zindians to showcase African AI innovation

Dubai, UAE May 5th, 2025 – Zindi, the leading professional network for data scientists and AI developers in emerging markets, is pleased to announce that it has signed a Memorandum of Understanding (MOU) with Cassava Technologies, a global technology leader of African heritage, to deliver artificial intelligence (AI) solutions and GPU-as-a-Service (GPUaas) across the African continent.   This…