#Machine Learning & AI

[dishachrista]

Discovering the Wonders of Machine Learning and Its Advantages:

Have you ever wondered how computers can learn and make choices all by themselves? It's like teaching them to be smart on their own. That's what machine learning is, and it's changing the way we do things. Let's take a journey to find out why machine learning is so amazing. We'll talk about things like helping computers understand languages, predicting the weather, and even teaching them to drive cars! So, let's explore the magic of machine learning.

The Good Things About Machine Learning:

Understanding InformationThink of machine learning as a super-smart helper that can deal with really big and complicated sets of information. Humans might get confused with all that data, but machine learning can assist us in making decisions based on it.

Changing the GameImagine you have a giant puzzle with lots of pieces. You know they make a picture, but it's just too hard to put it together by yourself. Machine learning is like a super-fast puzzle solver. It not only puts the puzzle together but also shows you amazing things about the picture that you might have missed.

Making Tasks SimpleOne cool thing about machine learning is that it can do boring and repetitive tasks for us. Think about typing lots of numbers into a computer spreadsheet – it can do that for us, and it hardly ever makes mistakes. This means we can use our time for more fun stuff.

Getting Things RightMachine learning models are really good at certain jobs. For example, they can tell if an email is spam much better than we can. That's super useful because we don't want our email inboxes to be filled with spam. It's like having a superhero to keep our emails clean.

Learning and Getting BetterThe more a machine learning model sees, the smarter it becomes. It learns from new information and gets better at what it does. This is really helpful in jobs where things change a lot, like predicting the stock market or understanding what people are talking about on social media.

Personalizing EverythingHave you ever noticed that websites like Netflix or Amazon suggest things you might like? That's machine learning in action. It watches what you do and recommends things you'll enjoy. It's like having a personal shopper who knows your tastes.

Handling Lots of StuffMachine learning models are like super chefs who can cook for a huge party. They can manage a massive amount of data and make quick decisions. This is really important for things like banks, which need to handle lots of transactions quickly and accurately.

Saving Time and MoneyMachine learning can save businesses a ton of time and money. For example, it can predict when machines in a factory might break, so they can be fixed before they cause big problems. This saves a lot of money because it's cheaper to fix things before they break.

Solving Tricky ProblemsSome problems are really tough, like understanding different languages, recognizing objects in pictures, or even playing complicated games. Machine learning can take on these challenges and find solutions that might be hard for humans.

Learning from AnythingMachine learning can learn from almost anything, even from things that don't seem like regular data. For example, it can learn from written words, pictures, or videos. This helps businesses understand what people think and like.

Quick Decision-MakingImagine being in a self-driving car. It needs to make fast decisions to keep you safe. Machine learning helps it do that by processing data from sensors and making decisions in real-time.

Creating Cool ThingsMachine learning is the technology behind cool stuff like virtual assistants (like Siri or Alexa) and language translation tools. These devices make life simpler and more enjoyable, like having a helpful friend who can speak every language.

Being Fair and JustSometimes, people can make unfair decisions because of their biases. Machine learning can be set up to be fair and impartial. It helps make choices in things like hiring or lending money based on facts, not feelings.

Driving DiscoveriesMachine learning isn't only for businesses; it also helps scientists. They use it to study complex data and make discoveries in areas like genetics, space exploration, and materials science. It's like having a super microscope that shows things we couldn't see before.

Protecting Our PlanetMachine learning can also help us take care of the environment. It's used to keep an eye on pollution, track changes in the climate, and predict natural disasters. By studying a lot of data, we can make better decisions to protect the Earth.

Improving HealthcareHealthcare is getting a boost from machine learning. It helps doctors diagnose diseases, discover new medicines, and keep track of patients. It's like having a super-smart medical assistant.

In summary, machine learning is a superpower that's changing the world. It makes handling data easy, automates tasks, personalizes experiences, and solves tough problems. It's fair, it's quick, and it's shaping the future. With machine learning, the possibilities are endless. If you want to learn more about machine learning or need help using it in your business, you can ask experts or take special courses. ACTE Institute offers courses that can give you the knowledge and skills to do well in this field. Think about all the amazing chances for analysing data, automating tasks, and making decisions that machine learning offers.

[destiel-news-network]

(Source)

[monsoon-of-art]

[dubaiseocompanyuae]

Understand SEO Better with Google Machine Learning Courses

Search Engine Optimization (SEO) is an essential tool for businesses looking to establish their presence online. With constant advancements in technology, machine learning has revolutionized how SEO strategies are implemented. By leveraging Google Machine Learning Courses, individuals and companies can gain a deeper SEO understanding and adapt to ever-changing algorithms. This article explores how machine learning integrates with SEO, the advantages it offers to businesses, and why collaborating with an SEO Company in Dubai, like dubaiseocompany.ae, can make a difference.

Why is Machine Learning Important in SEO?

Machine learning has transformed how search engines like Google rank websites. Algorithms powered by artificial intelligence (AI) can analyze vast amounts of data to identify patterns, interpret user behavior, and deliver more relevant search results. For businesses, understanding how these algorithms work is crucial to improving website rankings.

Reasons Machine Learning Matters in SEO:

Enhanced Personalization: Machine learning allows search engines to understand user intent better, delivering personalized search results based on past behavior and preferences.

Improved Keyword Targeting: By analyzing large datasets, machine learning helps identify high-value keywords, boosting website visibility.

Content Optimization: It evaluates the relevance and quality of content, ensuring it aligns with search engine expectations.

Automation: Routine tasks like backlink analysis and performance monitoring can be automated, saving time for strategic planning.

In a competitive market like Dubai, where businesses strive to rank higher, understanding machine learning’s impact is a game-changer. Partnering with a Dubai SEO Company ensures you utilize these advancements effectively.

What Google’s Machine Learning Courses Entail

Google Machine Learning Courses are designed to provide a comprehensive understanding of AI technologies and their applications. These courses cover essential concepts, making them suitable for both beginners and professionals.

Key Modules Covered:

Foundations of Machine Learning: Basics of algorithms and data analytics.

SEO Applications: How machine learning improves search rankings, keyword analysis, and content optimization.

Real-World Implementation: Practical case studies on integrating machine learning in SEO strategies.

Performance Metrics: Understanding analytics to track progress and optimize campaigns.

Benefits of Enrolling:

Gain practical knowledge of Google’s AI-powered tools.

Enhance your ability to analyze and implement data-driven SEO strategies.

Improve collaboration with an SEO Company in Dubai, such as dubaiseocompany.ae, by understanding advanced techniques.

These courses equip businesses and individuals with the skills to optimize their websites effectively, ensuring better results in the competitive landscape of SEO Dubai.

Benefits for Digital Marketing Services or Agencies

Digital marketing agencies, especially in a bustling market like Dubai, benefit immensely from machine learning integration. By adopting insights from Google Machine Learning Courses, they can provide more effective services to their clients.

Advantages for Digital Marketing Agencies:

Data-Driven Strategies: Machine learning tools provide actionable insights, enabling agencies to craft customized marketing plans.

Efficient Campaign Management: Automation tools powered by AI streamline processes like ad placement and budget optimization.

Improved ROI: With targeted strategies and enhanced analytics, businesses see higher returns on investment.

Competitive Edge: Staying updated with Google’s AI technologies ensures agencies stay ahead in the market.

For businesses in Dubai, collaborating with an experienced agency like dubaiseocompany.ae ensures that they benefit from these advanced techniques. Agencies equipped with machine learning knowledge can deliver better results in terms of visibility, traffic, and conversions.

Machine Learning in the Real-World Applications of SEO

Machine learning’s practical applications in SEO are vast and impactful. By understanding these real-world uses, businesses can leverage AI technologies to gain a competitive advantage.

Practical Applications Include:

Predictive Analytics: Forecasting trends and user behavior to optimize marketing strategies.

Content Recommendations: Personalized suggestions based on user preferences to improve engagement.

Voice Search Optimization: Adapting content for natural language processing to cater to voice searches.

Visual Search: Enhancing image optimization for platforms that prioritize visual content.

Spam Detection: Identifying low-quality backlinks and duplicate content to maintain website integrity.

These applications showcase how machine learning transforms SEO into a more dynamic and effective process. By enrolling in Google Machine Learning Courses, businesses can implement these strategies efficiently.

The Human Touch in Machine Learning-Driven SEO

While machine learning automates many SEO processes, the human element remains irreplaceable. Algorithms may analyze data, but creativity, intuition, and strategy come from human expertise.

Human Contributions to Machine Learning SEO:

Strategic Decision-Making: Algorithms provide data, but humans interpret it to create actionable strategies.

Content Creation: Engaging and high-quality content requires a human touch to connect with audiences emotionally.

Ethical Oversight: Ensuring that AI tools are used responsibly and align with business values.

Businesses in Dubai can benefit from the perfect blend of machine learning and human expertise by working with professionals at dubaiseocompany.ae. This ensures that technology is used to its fullest potential without losing the creative and ethical touch.

The Future of SEO: Machine Learning and Beyond

The role of machine learning in SEO is only expected to grow, shaping the future of digital marketing. Emerging trends indicate that businesses must adapt quickly to remain competitive.

Future Trends in SEO and Machine Learning:

Hyper-Personalization: Delivering highly tailored content for individual users.

AI-Powered Chatbots: Enhancing user experience and engagement.

Zero-Click Searches: Optimizing content for featured snippets and quick answers.

Augmented Reality (AR): Leveraging AR to create immersive brand experiences.

Staying ahead in the digital landscape requires continuous learning and adaptation. By combining Google Machine Learning Courses with expert guidance from an SEO company in Dubai, businesses can thrive in the evolving world of SEO Dubai.

Conclusion

Machine learning is transforming the way SEO strategies are developed and executed. From enhanced data analysis to predictive models, the possibilities are endless. Enrolling in Google Machine Learning Courses is an excellent way to enhance your SEO understanding and stay competitive in today’s digital market.

For businesses in Dubai, working with a reliable Dubai SEO Company like dubaiseocompany.ae ensures the perfect balance between advanced technology and expert human intervention. Ready to dominate search engine rankings? Visit dubaiseocompany.ae to explore how we can help you achieve your goals!

[pjoshi12]

Thriving In The AI Era

How Coditude's Developers Are Redefining Success in the AI Era

In the ever-changing tech landscape, the rise of generative artificial intelligence (AI) marks a profound shift in software development. Once confined to sci-fi, generative AI now leads innovation, transforming industries and developer roles. We're not just witnessing change; we're part of a revolution blending human creativity with powerful computing. This article examines how developers and AI synergize, exploring the uses of artificial intelligence, along with the challenges and opportunities, shaping the future of software development.

Understanding Generative AI

Generative AI, fueled by machine learning advancements, creates diverse content from text to images. Cloud computing's rise, exemplified by AWS, enables training complex AI models. Technologies like GPT-3 and Codex automate tasks, reshaping software development.

Generative AI challenges creativity and intelligence concepts, blurring lines between creators. Developers navigate AI's ethical and societal impacts while embracing its transformative potential. Generative AI augments human creativity, ushering in collaborative digital realms.

This future embraces a partnership between human ingenuity and machine intelligence, unlocking boundless possibilities.

Generative AI blurs lines between creators, challenging creativity and intelligence concepts. Developers navigate ethical implications while embracing its transformative potential.

Generative AI's Impact on Software Development

Generative AI revolutionizes software development, presenting new opportunities and challenges. It transforms how developers approach creativity and problem-solving. We explore its impact through real-world applications and developer perspectives.

Transforming Traditional Coding Practices

Generative AI, exemplified by tools like GitHub's Copilot, transforms coding practices by providing comprehensive code suggestions based on context. This departure from traditional programming accelerates development timelines, with developers reporting a 30% increase in productivity using AI-assisted tools. AI becomes a collaborative partner, generating sophisticated code snippets and reducing the need for extensive documentation and debugging.

Automating Documentation and Testing

Generative AI streamlines documentation by dynamically updating it based on code changes, enhancing code maintainability and reducing errors. In testing, AI-generated test cases cover diverse scenarios, improving software quality and reliability. This automation saves time and resources, transforming critical but tedious development tasks.

Case Studies: Real-world Applications

Generative AI drives diverse real-world applications in software development. For instance, a fintech firm modernized legacy systems by automating codebase conversion. Another startup enhanced user experience by dynamically generating interfaces based on user behavior data, boosting engagement.

Developer Perspectives: Opportunities and Challenges

Generative AI presents vast opportunities for software development, yet developers face challenges such as over-reliance and code originality concerns. Striking a balance between leveraging AI for productivity and maintaining a deep understanding of codebases is crucial.

Moreover, integrating generative AI raises questions about intellectual property rights and code ownership, given the complexity of determining the origin of AI-generated code snippets. Despite these challenges, generative AI transforms development by automating tasks, facilitating code generation, and expanding creative possibilities. However, navigating this landscape requires a thoughtful approach, an understanding of AI capabilities and limitations, and ongoing discussions about ethical implications.

Navigating the Generative AI Landscape

Integrating generative AI into software development requires a cultural shift, prompting developers to reevaluate skills and approaches for strategic adaptation in this new era.

Updating Skills and Knowledge

Generative AI demands skills beyond traditional programming, emphasizing AI fundamentals, data literacy, collaborative work, and ethical AI use.

Integrating Generative AI into Development Workflows

Successful integration entails incremental adoption, fostering a culture of experimentation, continuous learning, and balancing AI with human expertise.

Evolving Role of Creativity

Developers pivot towards creative problem-solving and strategic thinking, leveraging AI for experimentation and focusing on system architecture and user experiences. Interdisciplinary collaboration becomes vital as coding, design, and user experience converge in AI-driven applications.

Navigating generative AI demands technical proficiency, ethical awareness, and forward-thinking. Developers unlocking new possibilities for innovation and collaboration, shaping the future of technology in the digital age.

Future Workforce and Generative AI

Generative AI reshapes software development by altering job markets, skill requirements, and work nature, profoundly impacting the future workforce.

Balancing AI Automation

Generative AI impacts software development employment by automating tasks but also creating opportunities for human expertise to excel, emphasizing a need for a balanced approach where AI enhances human capabilities.

Impact on Developer Job Market

The job market shifts towards roles emphasizing oversight and creative use of AI tools, with traditional coding jobs evolving to focus on designing AI algorithms and integrating AI into system architectures, highlighting the need for adaptability and continuous learning.

Preparing Next-Gen Developers

Educational institutions adapt by incorporating AI fundamentals, ethical AI use, interdisciplinary skills, and lifelong learning into their curricula.

Overcoming Challenges and Ethical Considerations

Generative AI's rise in software development poses challenges and ethical dilemmas, from technical hurdles to philosophical considerations. Navigating this new era demands addressing these issues while upholding ethical standards, crucial for the development community and society.

Addressing the Fear of Obsolescence

Developers combat obsolescence fears by emphasizing irreplaceable skills like creative problem-solving and ethical decision-making. Lifelong learning ensures their continued relevance in an AI-driven future.

Navigating the Hype Versus Reality

Amidst utopian and dystopian narratives, a balanced view of generative AI is crucial. Stakeholders prioritize real-world utility over hype through rigorous testing and validation, ensuring genuine innovation.

Ethical Considerations and Responsibility

Generative AI presents ethical concerns like data privacy and algorithmic bias. Developers must prioritize transparency, accountability, and fairness in AI use to ensure responsible and equitable outcomes. This approach unlocks the true potential of AI while addressing ethical challenges.

Concluding Thoughts

The advent of generative AI marks an exciting yet challenging era in software development. As developers, we must harness this technology while navigating its ethical complexities. At Coditude, we recognize the importance of human creativity alongside AI's power, fostering an environment where developers can excel. By prioritizing continuous learning, ethical AI use, and a culture of innovation, Coditude is shaping the future of software development. Join us to be part of a community driving the generative AI revolution and creating a future where human ingenuity thrives.

[zrixinc]

Software developers are implementing. advanced cybersecurity measures such as data encryption and secure transactions.

[river-taxbird]

AI hasn't improved in 18 months. It's likely that this is it. There is currently no evidence the capabilities of ChatGPT will ever improve. It's time for AI companies to put up or shut up.

I'm just re-iterating this excellent post from Ed Zitron, but it's not left my head since I read it and I want to share it. I'm also taking some talking points from Ed's other posts. So basically:

We keep hearing AI is going to get better and better, but these promises seem to be coming from a mix of companies engaging in wild speculation and lying.

Chatgpt, the industry leading large language model, has not materially improved in 18 months. For something that claims to be getting exponentially better, it sure is the same shit.

Hallucinations appear to be an inherent aspect of the technology. Since it's based on statistics and ai doesn't know anything, it can never know what is true. How could I possibly trust it to get any real work done if I can't rely on it's output? If I have to fact check everything it says I might as well do the work myself.

For "real" ai that does know what is true to exist, it would require us to discover new concepts in psychology, math, and computing, which open ai is not working on, and seemingly no other ai companies are either.

Open ai has already seemingly slurped up all the data from the open web already. Chatgpt 5 would take 5x more training data than chatgpt 4 to train. Where is this data coming from, exactly?

Since improvement appears to have ground to a halt, what if this is it? What if Chatgpt 4 is as good as LLMs can ever be? What use is it?

As Jim Covello, a leading semiconductor analyst at Goldman Sachs said (on page 10, and that's big finance so you know they only care about money): if tech companies are spending a trillion dollars to build up the infrastructure to support ai, what trillion dollar problem is it meant to solve? AI companies have a unique talent for burning venture capital and it's unclear if Open AI will be able to survive more than a few years unless everyone suddenly adopts it all at once. (Hey, didn't crypto and the metaverse also require spontaneous mass adoption to make sense?)

There is no problem that current ai is a solution to. Consumer tech is basically solved, normal people don't need more tech than a laptop and a smartphone. Big tech have run out of innovations, and they are desperately looking for the next thing to sell. It happened with the metaverse and it's happening again.

In summary:

Ai hasn't materially improved since the launch of Chatgpt4, which wasn't that big of an upgrade to 3.

There is currently no technological roadmap for ai to become better than it is. (As Jim Covello said on the Goldman Sachs report, the evolution of smartphones was openly planned years ahead of time.) The current problems are inherent to the current technology and nobody has indicated there is any way to solve them in the pipeline. We have likely reached the limits of what LLMs can do, and they still can't do much.

Don't believe AI companies when they say things are going to improve from where they are now before they provide evidence. It's time for the AI shills to put up, or shut up.

[maodun]

shout out to machine learning tech (and all the human-input adjustment contributors) that's brought about the present developmental stage of machine translation, making the current global village 地球村 moment on rednote小红书 accessible in a way that would not have been possible years ago.

[bitchesgetriches]

31% of employees are actively ‘sabotaging’ AI efforts. Here’s why

"In a new study, almost a third of respondents said they are refusing to use their company’s AI tools and apps. A few factors could be at play."

[reasonsforhope]

"The first satellite in a constellation designed specifically to locate wildfires early and precisely anywhere on the planet has now reached Earth's orbit, and it could forever change how we tackle unplanned infernos.

The FireSat constellation, which will consist of more than 50 satellites when it goes live, is the first of its kind that's purpose-built to detect and track fires. It's an initiative launched by nonprofit Earth Fire Alliance, which includes Google and Silicon Valley-based space services startup Muon Space as partners, among others.

According to Google, current satellite systems rely on low-resolution imagery and cover a particular area only once every 12 hours to spot significantly large wildfires spanning a couple of acres. FireSat, on the other hand, will be able to detect wildfires as small as 270 sq ft (25 sq m) – the size of a classroom – and deliver high-resolution visual updates every 20 minutes.

The FireSat project has only been in the works for less than a year and a half. The satellites are fitted with custom six-band multispectral infrared cameras, designed to capture imagery suitable for machine learning algorithms to accurately identify wildfires – differentiating them from misleading objects like smokestacks.

These algorithms look at an image from a particular location, and compare it with the last 1,000 times it was captured by the satellite's camera to determine if what it's seeing is indeed a wildfire. AI technology in the FireSat system also helps predict how a fire might spread; that can help firefighters make better decisions about how to control the flames safely and effectively.

This could go a long way towards preventing the immense destruction of forest habitats and urban areas, and the displacement of residents caused by wildfires each year. For reference, the deadly wildfires that raged across Los Angeles in January were estimated to have cuased more than $250 billion in damages.

Muon is currently developing three more satellites, which are set to launch next year. The entire constellation should be in orbit by 2030.

The FireSat effort isn't the only project to watch for wildfires from orbit. OroraTech launched its first wildfire-detection satellite – FOREST-1 – in 2022, followed by one more in 2023 and another earlier this year. The company tells us that another eight are due to go up toward the end of March."

-via March 18, 2025

[dishachrista]

Getting Machine Learning Accessible to Everyone: Breaking the Complexity Barrier

Machine learning has become an essential part of our daily lives, influencing how we interact with technology and impacting various industries. But, what exactly is machine learning? In simple terms, it's a subset of artificial intelligence (AI) that focuses on teaching computers to learn from data and make decisions without explicit programming. Now, let's delve deeper into this fascinating realm, exploring its core components, advantages, and real-world applications.

Imagine teaching a computer to differentiate between fruits like apples and oranges. Instead of handing it a list of rules, you provide it with numerous pictures of these fruits. The computer then seeks patterns in these images - perhaps noticing that apples are round and come in red or green hues, while oranges are round and orange in colour. After encountering many examples, the computer grasps the ability to distinguish between apples and oranges on its own. So, when shown a new fruit picture, it can decide whether it's an apple or an orange based on its learning. This is the essence of machine learning: computers learn from data and apply that learning to make decisions.

Key Concepts in Machine Learning

Algorithms: At the heart of machine learning are algorithms, mathematical models crafted to process data and provide insights or predictions. These algorithms fall into categories like supervised learning, unsupervised learning, and reinforcement learning, each serving distinct purposes.

Supervised Learning: This type of algorithm learns from labelled data, where inputs are matched with corresponding outputs. It learns the mapping between inputs and desired outputs, enabling accurate predictions on unseen data.

Unsupervised Learning: In contrast, unsupervised learning involves unlabelled data. This algorithm uncovers hidden patterns or relationships within the data, often revealing insights that weren't initially apparent.

Reinforcement Learning: This algorithm focuses on training agents to make sequential decisions by receiving rewards or penalties from the environment. It excels in complex scenarios such as autonomous driving or gaming.

Training and Testing Data: Training a machine learning model requires a substantial amount of data, divided into training and testing sets. The training data teaches the model patterns, while the testing data evaluates its performance and accuracy.

Feature Extraction and Engineering: Machine learning relies on features, specific attributes of data, to make predictions. Feature extraction involves selecting relevant features, while feature engineering creates new features to enhance model performance.

Benefits of Machine Learning

Machine learning brings numerous benefits that contribute to its widespread adoption:

Automation and Efficiency: By automating repetitive tasks and decision-making processes, machine learning boosts efficiency, allowing resources to be allocated strategically.

Accurate Predictions and Insights: Machine learning models analyse vast data sets to uncover patterns and make predictions, empowering businesses with informed decision-making.

Adaptability and Scalability: Machine learning models improve with more data, providing better results over time. They can scale to handle large datasets and complex problems.

Personalization and Customization: Machine learning enables personalized user experiences by analysing preferences and behaviour, fostering customer satisfaction.

Real-World Applications of Machine Learning

Machine learning is transforming various industries, driving innovation:

Healthcare: Machine learning aids in medical image analysis, disease diagnosis, drug discovery, and personalized medicine. It enhances patient outcomes and streamlines healthcare processes.

Finance: In finance, machine learning enhances fraud detection, credit scoring, and risk analysis. It supports data-driven decisions and optimization.

Retail and E-commerce: Machine learning powers recommendations, demand forecasting, and customer behaviour analysis, optimizing sales and enhancing customer experiences.

Transportation: Machine learning contributes to traffic prediction, autonomous vehicles, and supply chain optimization, improving efficiency and safety.

Incorporating machine learning into industries has transformed them. If you're interested in integrating machine learning into your business or learning more, consider expert guidance or specialized training, like that offered by ACTE institute. As technology advances, machine learning will continue shaping our future in unimaginable ways. Get ready to embrace its potential and transformative capabilities.

[shygals-slophouse]

verdant garden

[stemgirlchic]

why neuroscience is cool

space & the brain are like the two final frontiers

we know just enough to know we know nothing

there are radically new theories all. the. time. and even just in my research assistant work i've been able to meet with, talk to, and work with the people making them

it's such a philosophical science

potential to do a lot of good in fighting neurological diseases

things like BCI (brain computer interface) and OI (organoid intelligence) are soooooo new and anyone's game - motivation to study hard and be successful so i can take back my field from elon musk

machine learning is going to rapidly increase neuroscience progress i promise you. we get so caught up in AI stealing jobs but yes please steal my job of manually analyzing fMRI scans please i would much prefer to work on the science PLUS computational simulations will soon >>> animal testing to make all drug testing safer and more ethical !! we love ethical AI <3

collab with...everyone under the sun - psychologists, philosophers, ethicists, physicists, molecular biologists, chemists, drug development, machine learning, traditional computing, business, history, education, literally try to name a field we don't work with

it's the brain eeeeee

[disease]

Frank Rosenblatt, often cited as the Father of Machine Learning, photographed in 1960 alongside his most-notable invention: the Mark I Perceptron machine — a hardware implementation for the perceptron algorithm, the earliest example of an artificial neural network, est. 1943.

[jaguarsoup]

Dragon…

||DO NOT FEED TO AI DO NOT USE FOR MACHINE LEARNING||