#AI Development Process

[mobiloitteptyltd]

South Africa’s First AI-Powered Hospital to Open in Johannesburg In an era marked by technological advancements, South Africa is poised to make history by unveiling the continent’s inaugural AI-powered hospital, set to open its doors in Johannesburg in 2024. This pioneering leap in healthcare promises to revolutionize medical diagnosis and treatment, significantly benefiting both healthcare providers and patients. As we delve deeper into this groundbreaking development, it becomes evident that AI is not just the future of healthcare in South Africa; it’s already playing a substantial role in transforming the landscape.

[beekeeperspicnic]

Giant hug for everyone who has been onto the game's Steam discussion board and replied with classy snark to people grumbling on there.

I'm reluctant to do that as a developer but when I see you guys doing it, just know that I'm cheering for you.

[timptoe]

I’m just so sad for BioWare, man. All that institutional memory, all that talent, sacrificed on the altar of corporate greed. Dragon Age deserved better, Mass Effect deserves better, than EA.

[vt-scribbles]

Something seriously lacking in my art is the ability to tell a story in a single illustration.

I've gotten so used to drawing my characters standing around doing random things that I've never practiced telling a full tale/putting implications into my pieces that require more thinking/looking.

It also comes from a lower amount of details in my works by default [since I like to get pieces done fast], but I'm tired of using that as an excuse.

[theeccentricraven]

World building in five

Thank you @kaylinalexanderbooks for the tag!

I also got tagged here so I owe another post too when I have another window of time 😉

Rules: post 3-5 pictures about a location from your WIP and remember to cite your sources and include image IDs to make your post accessible!

I did this here for Corpa, the setting of my YA Dystopia WIP, The Blood Cleaners. This post is for a location in Corpa called the Steel Castle, also known as The Steel.

I used Stable Diffusion to generate a couple of images of the Steel, both of which are not quite but close to how I envision the castle. The “castle” is technically a cluster of skyscrapers with many skyways connecting the towers. It’s on a hill, so you need to ride the aerial tram to get there. There is a radio tower on top where the president broadcasts propaganda to the whole region.

Source 1 (My own Instagram) | Source 2 (My own Instagram | Source 3 {Grendelkhan on Wiki Commons CC BY-SA 4.0 | Source 4 | Source 5 |

Tagging: @rickie-the-storyteller @buffythevampirelover @winterandwords @kitkins13 @kitty-is-writing @poethill @willtheweaver and OPEN

[phosphophylight-of-my-life]

rewatched key scenes from oshi no ko and now im in pain

[techdriveplay]

Why Quantum Computing Will Change the Tech Landscape

The technology industry has seen significant advancements over the past few decades, but nothing quite as transformative as quantum computing promises to be. Why Quantum Computing Will Change the Tech Landscape is not just a matter of speculation; it’s grounded in the science of how we compute and the immense potential of quantum mechanics to revolutionise various sectors. As traditional…

[jcmarchi]

Non-fiction books that explore AI's impact on society  - AI News

New Post has been published on https://thedigitalinsider.com/non-fiction-books-that-explore-ais-impact-on-society-ai-news/

Non-fiction books that explore AI's impact on society  - AI News

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Artificial Intelligence (AI) is code or technologies that perform complex calculations, an area that encompasses simulations, data processing and analytics.

AI has increasingly grown in importance, becoming a game changer in many industries, including healthcare, education and finance. The use of AI has been proven to double levels of effectiveness, efficiency and accuracy in many processes, and reduced cost in different market sectors. 

AI’s impact is being felt across the globe, so, it is important we understand the effects of AI on society and our daily lives. 

Better understanding of AI and all that it does and can mean can be gained from well-researched AI books.

Books on AI provide insights into the use and applications of AI. They describe the advancement of AI since its inception and how it has shaped society so far. In this article, we will be examining recommended best books on AI that focus on the societal implications.

For those who don’t have time to read entire books, book summary apps like Headway will be of help.

Book 1: “Superintelligence: Paths, Dangers, Strategies” by Nick Bostrom

Nick Bostrom is a Swedish philosopher with a background in computational neuroscience, logic and AI safety. 

In his book, Superintelligence, he talks about how AI  can surpass our current definitions of intelligence and the possibilities that might ensue.

Bostrom also talks about the possible risks to humanity if superintelligence is not managed properly, stating AI can easily become a threat to the entire human race if we exercise no control over the technology. 

Bostrom offers strategies that might curb existential risks, talks about how Al can be aligned with human values to reduce those risks and suggests teaching AI human values.

Superintelligence is recommended for anyone who is interested in knowing and understanding the implications of AI on humanity’s future.

Book 2: “AI Superpowers: China, Silicon Valley, and the New World Order” by Kai-Fu Lee

AI expert Kai-Fu Lee’s book, AI Superpowers: China, Silicon Valley, and the New World Order, examines the AI revolution and its impact so far, focusing on China and the USA. 

He concentrates on the competition between these two countries in AI and the various contributions to the advancement of the technology made by each. He highlights China’s advantage, thanks in part to its larger population. 

China’s significant investment so far in AI is discussed, and its chances of becoming a global leader in AI. Lee believes that cooperation between the countries will help shape the future of global power dynamics and therefore the economic development of the world.

In thes book, Lee states AI has the ability to transform economies by creating new job opportunities with massive impact on all sectors. 

If you are interested in knowing the geo-political and economic impacts of AI, this is one of the best books out there. 

Book 3: “Life 3.0: Being Human in the Age of Artificial Intelligence” by Max Tegmark

Max Tegmark’s Life 3.0 explores the concept of humans living in a world that is heavily influenced by AI. In the book, he talks about the concept of Life 3.0, a future where human existence and society will be shaped by AI. It focuses on many aspects of humanity including identity and creativity. 

Tegmark envisions a time where AI has the ability to reshape human existence. He also emphasises the need to follow ethical principles to ensure the safety and preservation of human life. 

Life 3.0 is a thought-provoking book that challenges readers to think deeply about the choices humanity may face as we progress into the AI era. 

It’s one of the best books to read if you are interested in the ethical and philosophical discussions surrounding AI.

Book 4: “The Fourth Industrial Revolution” by Klaus Schwab

Klaus Martin Schwab is a German economist, mechanical engineer and founder of the World Economic Forum (WEF). He argues that machines are becoming smarter with every advance in technology and supports his arguments with evidence from previous revolutions in thinking and industry.

He explains that the current age – the fourth industrial revolution – is building on the third: with far-reaching consequences.

He states use of AI in technological advancement is crucial and that cybernetics can be used by AIs to change and shape the technological advances coming down the line towards us all.

This book is perfect if you are interested in AI-driven advancements in the fields of digital and technological growth. With this book, the role AI will play in the next phases of technological advancement will be better understood.

Book 5: “Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy” by Cathy O’Neil

Cathy O’Neil’s book emphasises the harm that defective mathematical algorithms cause in judging human behaviour and character. The continual use of maths algorithms promotes harmful results and creates inequality.

An example given in  the book is of research that proved bias in voting choices caused by results from different search engines.

Similar examination is given to research that focused Facebook, where, by making newsfeeds appear on users’ timelines, political preferences could be affected.

This book is best suited for readers who want to adventure in the darker sides of AI that wouldn’t regularly be seen in mainstream news outlets.

Book 6: “The Age of Em: Work, Love, and Life when Robots Rule the Earth” by Robin Hanson

An associate professor of economics at George Mason University and a former researcher at the Future of Humanity Institute of Oxford University, Robin Hanson paints an imaginative picture of emulated human brains designed for robots. What if humans copied or “emulated” their brains and emotions and gave them to robots?

He argues that humans who become “Ems” (emulations) will become more dominant in the future workplace because of their higher productivity.

An intriguing book for fans of technology and those who love intelligent predictions of possible futures.

Book 7: “Architects of Intelligence: The truth about AI from the people building it” by Martin Ford

This book was drawn from interviews with AI experts and examines the struggles and possibilities of AI-driven industry.

If you want insights from people actively shaping the world, this book is right for you!

CONCLUSION

These books all have their unique perspectives but all point to one thing – the advantages of AI of today will have significant societal and technological impact. These books will give the reader glimpses into possible futures, with the effects of AI becoming more apparent over time.

For better insight into all aspects of AI, these books are the boosts you need to expand your knowledge. AI is advancing quickly, and these authors are some of the most respected in the field. Learn from the best with these choice reads.

[foxmulderautism]

like yeah dude it’s really cool that you used AI to complete an art piece that was purposefully left unfinished because the artist was dying of AIDS as the government ignored the mass loss of life and health. all you really did was show that technology can bring an art piece into the modern age but why? why do we need to do that? what does it say about us if we feel that a piece needs to be ‘completed’? how are we viewing completion?? can seeing an ai generated “completion” of unfinished have the same effect of seeing the parts haring left blank and the parts where the art drips into the blank? does it aid the narrative of it in any way? can AI understand the levels at which artists used their pieces about AIDS as a form of protest? of begging to be seen? how can it when an AI’s concept of ‘completing’ the piece is just guesswork of what the colours and shapes would look like to match what haring made which is nowhere near the same level of intention that came from him? you said you ‘completed’ the piece with ai because his story is so sad but does that mean we should try to rectify sadness by getting rid of the representation of it? should we not continue to sit in the sadness and discomfort that unfinished and other AIDS inspired art asks us to do because those feelings are only a fraction of what the people who died and lost felt, is that not the least we can do for them? and what good does any of this actually do when we can use technology to ‘complete’ a purposefully unfinished art piece about an artist’s untimely death from AIDS but we can never bring keith haring or any other person who died of AIDS back to life? where does haring, the person whose illness and death lives in those blanks, come into your self fulfilling ai generated completion of his work?

[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.

[itsclydebitches]

Something I don't think we talk enough about in discussions surrounding AI is the loss of perseverance.

I have a friend who works in education and he told me about how he was working with a small group of HS students to develop a new school sports chant. This was a very daunting task for the group, in large part because many had learning disabilities related to reading and writing, so coming up with a catchy, hard-hitting, probably rhyming, poetry-esque piece of collaborative writing felt like something outside of their skill range. But it wasn't! I knew that, he knew that, and he worked damn hard to convince the kids of that too. Even if the end result was terrible (by someone else's standards), we knew they had it in them to complete the piece and feel super proud of their creation.

Fast-forward a few days and he reports back that yes they have a chant now... but it's 99% AI. It was made by Chat-GPT. Once the kids realized they could just ask the bot to do the hard thing for them - and do it "better" than they (supposedly) ever could - that's the only route they were willing to take. It was either use Chat-GPT or don't do it at all. And I was just so devastated to hear this because Jesus Christ, struggling is important. Of course most 14-18 year olds aren't going to see the merit of that, let alone understand why that process (attempting something new and challenging) is more valuable than the end result (a "good" chant), but as adults we all have a responsibility to coach them through that messy process. Except that's become damn near impossible with an Instantly Do The Thing app in everyone's pocket. Yes, AI is fucking awful because of plagiarism and misinformation and the environmental impact, but it's also keeping people - particularly young people - from developing perseverance. It's not just important that you learn to write your own stuff because of intellectual agency, but because writing is hard and it's crucial that you learn how to persevere through doing hard things.

Write a shitty poem. Write an essay where half the textual 'evidence' doesn't track. Write an awkward as fuck email with an equally embarrassing typo. Every time you do you're not just developing that particular skill, you're also learning that you did something badly and the world didn't end. You can get through things! You can get through challenging things! Not everything in life has to be perfect but you know what? You'll only improve at the challenging stuff if you do a whole lot of it badly first. The ability to say, "I didn't think I could do that but I did it anyway. It's not great, but I did it," is SO IMPORTANT for developing confidence across the board, not just in these specific tasks.

Idk I'm just really worried about kids having to grow up in a world where (for a variety of reasons beyond just AI) they're not given the chance to struggle through new and challenging things like we used to.

[jcmarchi]

From Recurrent Networks to GPT-4: Measuring Algorithmic Progress in Language Models - Technology Org

New Post has been published on https://thedigitalinsider.com/from-recurrent-networks-to-gpt-4-measuring-algorithmic-progress-in-language-models-technology-org/

From Recurrent Networks to GPT-4: Measuring Algorithmic Progress in Language Models - Technology Org

In 2012, the best language models were small recurrent networks that struggled to form coherent sentences. Fast forward to today, and large language models like GPT-4 outperform most students on the SAT. How has this rapid progress been possible? 

Image credit: MIT CSAIL

In a new paper, researchers from Epoch, MIT FutureTech, and Northeastern University set out to shed light on this question. Their research breaks down the drivers of progress in language models into two factors: scaling up the amount of compute used to train language models, and algorithmic innovations. In doing so, they perform the most extensive analysis of algorithmic progress in language models to date.

Their findings show that due to algorithmic improvements, the compute required to train a language model to a certain level of performance has been halving roughly every 8 months. “This result is crucial for understanding both historical and future progress in language models,” says Anson Ho, one of the two lead authors of the paper. “While scaling compute has been crucial, it’s only part of the puzzle. To get the full picture you need to consider algorithmic progress as well.”

The paper’s methodology is inspired by “neural scaling laws”: mathematical relationships that predict language model performance given certain quantities of compute, training data, or language model parameters. By compiling a dataset of over 200 language models since 2012, the authors fit a modified neural scaling law that accounts for algorithmic improvements over time. 

Based on this fitted model, the authors do a performance attribution analysis, finding that scaling compute has been more important than algorithmic innovations for improved performance in language modeling. In fact, they find that the relative importance of algorithmic improvements has decreased over time. “This doesn’t necessarily imply that algorithmic innovations have been slowing down,” says Tamay Besiroglu, who also co-led the paper.

“Our preferred explanation is that algorithmic progress has remained at a roughly constant rate, but compute has been scaled up substantially, making the former seem relatively less important.” The authors’ calculations support this framing, where they find an acceleration in compute growth, but no evidence of a speedup or slowdown in algorithmic improvements.

By modifying the model slightly, they also quantified the significance of a key innovation in the history of machine learning: the Transformer, which has become the dominant language model architecture since its introduction in 2017. The authors find that the efficiency gains offered by the Transformer correspond to almost two years of algorithmic progress in the field, underscoring the significance of its invention.

While extensive, the study has several limitations. “One recurring issue we had was the lack of quality data, which can make the model hard to fit,” says Ho. “Our approach also doesn’t measure algorithmic progress on downstream tasks like coding and math problems, which language models can be tuned to perform.”

Despite these shortcomings, their work is a major step forward in understanding the drivers of progress in AI. Their results help shed light about how future developments in AI might play out, with important implications for AI policy. “This work, led by Anson and Tamay, has important implications for the democratization of AI,” said Neil Thompson, a coauthor and Director of MIT FutureTech. “These efficiency improvements mean that each year levels of AI performance that were out of reach become accessible to more users.”

“LLMs have been improving at a breakneck pace in recent years. This paper presents the most thorough analysis to date of the relative contributions of hardware and algorithmic innovations to the progress in LLM performance,” says Open Philanthropy Research Fellow Lukas Finnveden, who was not involved in the paper.

“This is a question that I care about a great deal, since it directly informs what pace of further progress we should expect in the future, which will help society prepare for these advancements. The authors fit a number of statistical models to a large dataset of historical LLM evaluations and use extensive cross-validation to select a model with strong predictive performance. They also provide a good sense of how the results would vary under different reasonable assumptions, by doing many robustness checks. Overall, the results suggest that increases in compute have been and will keep being responsible for the majority of LLM progress as long as compute budgets keep rising by ≥4x per year. However, algorithmic progress is significant and could make up the majority of progress if the pace of increasing investments slows down.”

Written by Rachel Gordon

Source: Massachusetts Institute of Technology

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[atcuality1]

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[solutionmindfire]

The healthcare industry is constantly striving to improve efficiency, accuracy, and patient care. While dedicated medical professionals are the heart of any hospital, Information Technology (IT) offers a powerful tool to revolutionize day-to-day operations through automation. By leveraging automation technologies like Robotic Process Automation (RPA) and Artificial Intelligence (AI), hospitals can streamline workflows, reduce errors, and empower staff to focus on what matters most: their patients.

[atcuality3]

Engineering Excellence into Every Digital Experience - Atcuality

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