#alg method

[rigelmejo]

Picking up a second language from television: an autoethnographic L2 simulation of L1 French learning

I deeply appreciate this experiment one person did with their own language learning, Picking up a second language from television: an autoethnographic L2 simulation of L1 French learning, and it's free to read if anyone else is curious.

The writer did the following: attempted to learn French by watching TV shows in French with no subtitles, and no word lookups or translations, for 1200 hours. They started with shows for adults, and realized children's cartoons were much easier to learn new words from initially as the visual context and slow speech helped them figure out word/phrase/grammar meanings, and then watched children's cartoons for a while until gradually increasing difficulty of shows again. While in the first several hundred hours, they watched some cartoons by repeatedly watching certain sentences and scenes over and over, attempting to understand as much as they could, such as with the cartoon Calliou. After 1200 hours, they started reading French, talking to people in French, and reading some grammar explanations at that point. They passed a B2 test at the conclusion of 1500 hours, with the first 1200 being watching French TV with no tools/explanations and then the last 300 hours including speaking and finally looking up some explanations and seeing french writing.

This account of their experience is incredibly interesting to me. It shows a few things which (at least for me) can be learned from.

1. That a goal of aiming for over a thousand hours spent trying to understand materials in your target language is useful.

2. The time they spent engaging with French is (very roughly) similar to FSI estimates if you include the hours of outside-class study recommended, 750 class hours plus time spent studying outside of class (2 hours outside of class per class hour is the FSI recommendation - which would be 2250 for French). The time it took him to pass B2 is in the 1000+ hour range, which is similar to classroom time plus outside study time expected. Automatic Language Growth type courses tend to suggest 1000-2000 hours to speak on an adult level and understand others, with 1500 being the suggested study length Dreaming Spanish suggests and ALG Thai programs recommending 2000 hours. Some learners who've done DS and ALG Thai programs suggest they feel they can understand people and discuss things on a basic level at those hours, but feel they need more hours to really be 'fluent'. I think that aligns well with the 1500 hour French study linked above, where he felt 1500 hours felt intermediate and capable of talking with others/working/understanding others but by no means fluent. So my personal thoughts on this is... the French 2250 hour estimate (FSI class-hours estimates added to 2 outside-class hours per hour as FSI suggests) is probably roughly in line with people's experiences.

And the earlier goal of 1500 (1000-2000 depending on the individual, and the target language) being a good initial goal for basic ability to do all things in the language (but not necessarily well and not mastered). Perhaps this number could be several hundred hours, and less than a thousand, if your target language is similar to one you already know or you have experience learning languages already. But the thought I am concluding from all of this is: expect 1000 hours or more trying to understand stuff in your target language if you wish to be able to understand the main idea (or more) of most things, and communicate your main idea with others.

(And for the sake of curiosity, FSI estimates 2200 class hours for Japanese and Chinese, so 6600 hours total, for an English speaker. So probably...at minimum 1000 hours to start speaking, like ALG Thai learners notice, at minimum 2000 hours to start understanding the main idea of most things, and based on FSI estimates... perhaps 3000-3300 hours minimum to start feeling similar to that level achieved after 1500 hours studying French or Spanish).

3. He studied French with zero aids like grammar guides or translations or even the French alphabet and a pronunciation explanation. He mentions in his paper, that being able to look up translations, or even see French subtitles on the TV shows, may have potentially sped up his progress. (Or perhaps not, as he didn't try those tools until 1200 hours in). Many of us learners HAVE used such tools already. The lesson I take from his experience is...even if you use NO tools or aids to learn, if you dedicate 1000+ hours to attempting to understand visual-audio situations (videos or classes or your life experiences in a country) you will make progress and increase your understanding of the language. If you initially focus on more-visually understandable things, like children's cartoons or ALG teachers who visually attempt to explain or a person helping you through a situation (like a native speaker talking to you as they help you grocery shop), then your initial progress as a beginner will be FASTER. And it may well be necessary to understand a certain amount, as a beginner, for the target-language input to be useful. You don't need to understand 100% or even 90%, but you do need to understand enough to hear at least 1 word or phrase or grammar piece every couple minutes that you can GUESS at the possible meaning of. At least, to learn in a timely manner.

So as a beginner, visual-audio input is much more useful than audio only - especially if you don't have cognates to use to make guesses. And visual-audio input where the speaking is ABOUT something in the same scene/experience/event so it's easier to guess what bits of the language mean. And if you choose to use tools like a translation app/site, if it's helping you figure out meaning of bits of language then it may be particularly useful as a beginner. (There's certainly language learning camps that think using translations lessens your actual learning of the language, but based on his paper... I at least think, what I take from it, is that those beginning few hundred hours it's most important you find a way to UNDERSTAND the main idea of the target language material. My take is that, even if that involves translation tools for 300 hours initially, it's worth it. You can abandon translation tools once you understand enough stuff in the language - like a few hundred key words or pronunciations etc - to start understanding really basic main ideas from kids cartoons. But if you can't even guess "cartoon character is pointing to bike, seems to want bike, even though I have no idea what words they're saying, maybe one of those words was bike..." then you aren't going to comprehend enough to guess word meanings. It seems like after the first few hundred hours, the need for translations and/or kid cartoons is less. Once you have some small base of words/phrases you've guessed the meaning of, then it's possible to start guessing the meaning of conversations even when there's no visual context to indicate what's going on - such as adult shows where they discuss off-screen abstract topics, and audio only materials).

4. There is no huge need to pick the 'perfect' study method or materials. After the initial beginner stage of learning some key words/phrases from visual context (a few hundred hours), you WILL continue learning and make progress as long as you keep engaging with the language and trying to understand the main idea. So study/watch/listen to whatever you like that, that you can get yourself to engage with for 1000 hours or more. Some people will want to keep looking up word-translations, do that. Some people will love cdramas or anime or shows and just want to watch tons of shows. Some people will feel more comfortable watching/doing easier things like a tutor that matches your comfort level (like crosstalk), immersion with someone helping you navigate, watching cartoons, watching stuff for learners (like Comprehensible Input youtube channels). Some people want to jump into the deep end and go for audiobooks or podcasts. If you are able to even just GUESS a word/phrase/grammar point meaning every 1-3 minutes (or more often) then you'll likely keep improving your understanding. No need to be perfect, just figure out a way to keep yourself engaged. Because it'll take a thousand hours or more.

5. I hate to say this because I love reading... but to develop listening comprehension... you need to listen. Having visual-audio materials as a beginner is critical. Even if that means graded readers you read paired with an audiobook. And you'll need to keep listening for at least 1000 hours to build good listening comprehension - it takes time to get used to hearing the pronunciation, to mentally separating it into phrases/words, to adjusting to various speeds, to emotional meanings and implications, to adjust to understanding various accents. His paper indicated he struggled with understanding faster speech until he'd studied enough hundreds of hours, and then struggled with slang and accents much longer. Listening comprehension is critical to: conversing with others, speaking and being understood, listening to shows and audio. So it must be worked on. That is not to say you can't study by reading - I sure did! And still do! But that the hours spent reading WITHOUT audio will not contribute to some of those critical listening and speaking skills.

Reading on it's own will help prime you to pick up vocabulary when listening faster, help with increasing vocabulary, help with getting used to word usage and grammar. But based on his paper... for him, at least, it seems reading skill was picked up Extremely Fast after already having a good ability to listen and speak with people. He picked up reading skills within months! From my own experience... I mostly studied with reading ONLY activities, in French and Chinese, and improving in my listening skills takes A LOT of hours. It will not be as good as my reading within a few months. I think I may pick up listening skills Somewhat faster than someone who's read less, since I am primed to learn listening comprehension of words I understand in reading faster than trying to comprehend brand new words. But so many listening skills are lagging significantly. My Chinese listening skills are much better than my French listening skills, since I did often listen while reading when I studied. But there's still so many key aspects of words that I don't have natural ability to simply verbalize without thinking, like instantly saying the right tone, or instantly knowing the right pronunciation for some words I can read fine. And comprehension of listening to people is way lower than my ability to read and comprehend.

[squeakygeeky]

Three months of Thai

I've reached a weird point where listening to Thai, even native content like unsubbed actor interviews, definitely feels like I'm listening to normal speech with words, but where only some of those words are familiar and only some of them have real meaning to me. Definitely a contrast to Vietnamese, where I can pick out a few basic words, but it still just sounds alien overall, and Spanish where I understand the majority of what I hear. What I do know isn't just nouns and a few verbs, I've started catching functional things like 'but' and 'like this.'

I did have an actual lesson where I spoke enough to answer yes/no questions and say how may things I had and what color, so I feel like that proved to myself that speaking can naturally arise out of listening to a some degree. I do have another lesson tomorrow but I'm thinking of stopping. It's making this feel a little more like a REAL THING I'm doing and not just a weird little side project and I'm not sure how much mental capacity I have for taking on a REAL THING right now because REAL THINGS can make me stressed in a way thing things that aren't really a thing don't.

I'm sick of beginner content, but I'm not ready for anything more interesting, so I just have to plow through. I'll have some much less time/energy after this coming weekend so no more 2hrs a day, I'll be lucky to manage 1 and I'm sad about that despite how much of a slog it can feel like. There's also a part of me that wants to try a new language now now now, but I also love Thai and would miss it.

[autistme]

i swear theres a 5x5+ parity alg for stupid dumb babies but all the ones on kewbs require a cube rotation and im not paid enough to remember my XYZs or my MSEs

[shoehorseconstant]

every once in a while i m like AH I NEED TO RELEARN VECTOR CALCULUS so i look @ my beautiful gorgeous stewart textbook pdf and it is so lovely—but then i have to remember that i actually didn't follow stewart that carefully and made a lot of shit up and then i have to go look at my purple notebook filled with my own homework to remember the previous life i used to live. and i have to wonder how i had the time and brains to do all of that..... and should i write my own vector calculus textbook

[ansu-gurleht]

hm. for some reason my 5x5 rubik's cube is sitting on my desk corner looking at me like "hey! you should dust me off (i'm so dirty) and scramble me and solve me again. you know you want to!" and i'm struggling to find a reason not to

[smidge-j]

Done

Orb time

[zimmbzon]

I absolutely need to write a proper post about how I’m learning Thai via the Automatic Language Growth method (chur @visualtaehyun for the shoutout) and I will after my interview tomorrow.

But for right now, enjoy this screenshot of Khroo Ying and Khruu Fah from when they were talking about a Bangkok of the not too distant future. A Bangkok that has gender neutral toilets beside the family room, the women’s bathroom and the men’s bathroom. Because for some people the women’s bathroom isn’t right for them, but neither is the men’s.

I cried. To be seen, I cried.

source after the jump

source: Comprehensible Thai YT 20:52

if you are interested in Comprehensible Thai and the ALG method click the video description for info.

[cubingciruitblog]

What Speedcubing can Teach... #1

I've been cubing for almost 2 whole years now, and apart from finding a new hobby and passion, I feel that speedcubing has taught me much more too. This is a (lengthy) blog on what I've learnt through cubing. I'll be writing about a lesson that cubing has taught me every 2 days for the rest of the week so check back to see more. Short Intro - How I got into cubing Of course, the first big step would be finally solving your cube for the first time and seeing all 6 colours solved (not by peeling the stickers). But I really was fascinated by this puzzle. It looked so simple at first, but when I actually tried solving it, well of course I did not succeed. However, I was determined to solve my cube. I spent days trying to learn off YouTube videos. And one day, I did it. No more messing up in the middle of trying to return it to its solved state. I remember holding it up in front of my family proudly. So anyways, after that I saw some videos of speedcubers solving it faster than the time it took for me to type this sentence. And that's when I started trying to improve my timings and really learnt a lot from cubing... Lesson 1 To improve, I first decided, hey why not learn a faster method, knowing that probably wouldn't get far with the beginners' method. Like most cubers, I chose CFOP, mainly because it was similar to beginners' and therefore easier to grab the concepts. I started by learning cross, taking it a step at a time. So I tried making a white cross directly, instead of a daisy. I thought that I would be much faster at once. Who knew? It took me longer to make a white cross than a daisy. I was sort of disappointed but I decided to give it a chance. So I practised a lot and looked at other cubers' ways of making the cross, practised the techniques and a started getting the hang of it. I'll come back to this later but let me give another example. Now the next part I learned was 2-Look PLL, a set of algorithms. It was tortuous memorising them. Everytime I tried using the algs. in solves, I would just mess up. What did I do? I told myself to keep practising over and over again. I'd start the moment I had time, clocking in as much practice as possible. It was hard, but worth it. My times then, were now halved as compared to when I was still using beginners'. Back to cross, I just want to compare how I put in the effort to practise to my brother. My brother can solve a cube. He uses beginners' and I tried teaching him the cross. However, he too ran into the problem of his daisy being faster. He gave up. I tried convincing him that eventually, as long as he was willing to practise, it would help a ton. But I guess it's not easy to change someone's mind. Just because of our different attitudes, I'm 4-5 times faster than him. So what is my takeaway and the lesson here? I think it's quite obvious by now but here it is anyways : Although practice can never make you a perfect person at whatever you are doing, it can make you much better. Hard work pays off and overall, this makes me understand the concept behind delayed gratification. This is the end my blog for today

~ CubingCircuit, 18 July 2023 (from where I am)

[aria-i-adagio]

I have comments.

First of all, I don't presume to have 'the answer.' In fact, I would strongly argue that there isn't a singular answer to the problems in education. That said -

The article raises the issue of the significant amount of federal funding that has been granted to schools for pandemic recovery. Money is all well and good, but we still run into multiple problems. One, staffing shortages. The pay for teaching sucks, but it isn't a desirable job for more reasons than remuneration. (The crushing weight of metrics/growth/achievement/testing.) Then there's the politicization of teaching and teachers.

Beyond staff shortages, there's simply the issue of time. Sure, some students will be able to cram about three years worth of learning into one. Most can't. And children and teens still need time to just be children and teens; they don't need to be spending hours after school or during breaks playing catch up. Allowing students to attend a fifth year of high school to meet graduation requirements, if necessary, would be a significant help. With dual enrollment options, a student who is, say, strong in ELA but weak in math, could start their gen ed requirements and have time to actually learn the math. Students don't deserve to be put through a meat grinder any more than teachers.

Finally, the notion that the metrics used for school accountability have ever been fair is laughable. The methods used to calculate 'growth scores' aren't just arcane - they're proprietary. So, not only do I not have sufficient information to determine whether the end of course test is valid*, I have no way to access the validity of the way I'm scored. And I don't trust testing companies further than I can pick them up and throw them. Oh, and the main algorithm was developed based off a method to evaluate the effect of beef herd sires. Just going to throw that out there.

*I'm dubious of the validity of the data, because it is most definitely NOT used to make reasonable changes to educational policy or curriculum. If the oh-so-valid tests are indicating across the state that a plurality of students entering high school don't have basic proficiency in middle school math, and that proficiency levels in math drop each year because of the snowball effect of missing prerequisite skills, a reasonable person might think that adjusting the curriculum to give freshman an opportunity to gain basic pre-algebra skills (front-ending the fourth year of math) would make more sense than back-ending the joke of a class called Bridge Math. I'm not arguing to holding back students from entering high school. I am arguing for students who haven't demonstrated basic proficiency with middle school math to have a math track which looks like this: pre-algebra, algebra 1, geometry/algebra 2 (geo and alg 2 are sometimes switched in sequence) instead of alg 1, geo/alg 2, and 'a fourth higher level math class.'

But it's easier to blame teachers, I suppose.

[normanthedove]

AI(alg*) = AS: A COMPLETE DISSECTION OF DR. TIM KING'S, MD., ATTEMPTED PATENT FRAUD AND HOW U.S. TAXPAYER ARE BEING BILKED $BILLION$ BY HIS SYSTEM: EVALUATING SERIOUS FLAWS IN DR. KING'S, "FORENSIC SYSTEM AND METHODS OF DETECTING FRAUD, ABUSE, & DIVERSION IN PRESCRIPTION USE OF CONTROLLED SUBSTANCES," A COMPLEX PODCASTS ANALYSIS-REVIEW !!!FOR THE EYES OF HEAVY WEIGHTS ONLY!!!

Nina Simone – Sinnerman (Audio) NORMAN J CLEMENT RPH., DDS, NORMAN L. CLEMENT PHARM-TECH, MALACHI F. MACKANDAL PHARMD, BELINDA BROWN-PARKER, IN THE SPIRIT OF JOSEPH SOLVO ESQ., INC.T. SPIRIT OF REV. IN THE SPIRIT OF WALTER R. CLEMENT BS., MS, MBA. HARVEY JENKINS MD, PH.D., IN THE SPIRIT OF C.T. VIVIAN, JELANI ZIMBABWE CLEMENT, BS., MBA., IN THE SPIRIT OF THE HON. PATRICE LUMUMBA, IN THE SPIRIT…

[squeakygeeky]

100 hours of Thai listening comprehension

I did just talk bout my one month milestone which was 60 hours so it's not like I feel much different, but supposedly I should know around ~300 words now. Do I? I have no idea because this isn't a method that lends it self to tracking and testing. I am pretty good at colors, numbers, animals, family and food so I guess that's about right. I can feel my listening comprehension improving week by week when I watched subtitled BL. I believe this would be A1 CEFR, but only for listening. No speaking, reading, or writing.

I'm probably more advanced than 100hrs would normally would be since I would estimate I've watched 350+ hours of Thai BL (yikes!). But I don't count that since I wasn't paying attention to the language, and even now if I watch something with subtitles I'm mostly learning subtitles. I can definitely understand the words I know when I hear them in BL and not just in the context of the lessons. There are some words that I recognize as distinct words, but I'm still trying to figure out what the heck they mean.

I'm avoiding watching Thai BL for the most part, since I'm kind of saving it all for later when I understand even more. Right not it's only Laws of Attraction. Knowing family words helped with the very poor subtitles though, so my time is paying off.

I am very bored of watching videos like Guess The Fruit and look forward to being able to watch more advanced ones about Thai culture and stuff, but that's a ways off. Watching BL without subtitles (not in the sense of understanding all the words, but being able to follow the plot) would I think take almost a year, even at my current pace, which is absolutely going to be unsustainable when we hit fall. But I keep thinking of that meme 'the time will pass anyway.'

[the-frosty-mac]

hm. might refocus my japanese learning to an ALG method

[blogbyahad]

How Quantum Algorithms Revolutionize Financial Portfolio Optimization for Risk Management

In the fast-paced world of finance, portfolio optimization is a critical component of risk management. Traditional methods often struggle to handle the complexities and vast datasets involved, leading to suboptimal decision-making. However, quantum algorithms are emerging as a powerful alternative, offering new avenues for enhancing financial portfolio optimization. Let’s explore how these algorithms revolutionize risk management in finance.

Understanding Portfolio Optimization

Portfolio optimization involves selecting the best mix of assets to maximize returns while minimizing risk. Key challenges include:

Complex Calculations: Evaluating potential asset combinations requires significant computational resources, especially as the number of assets increases.

Dynamic Market Conditions: Financial markets are volatile and influenced by numerous unpredictable factors, making it essential to adapt quickly.

Nonlinear Relationships: Asset correlations can be complex and nonlinear, complicating risk assessment.

Advantages of Quantum Algorithms

Increased Computational Power:

Exponential Speedup: Quantum algorithms can perform complex calculations much faster than classical computers. For example, using Grover’s algorithm, the search for optimal asset combinations can be accelerated, enabling quicker decision-making.

Simultaneous Processing: Quantum systems can analyze multiple potential portfolios at once, significantly reducing the time required for evaluations.

2. Improved Risk Assessment:

Advanced Models: Quantum algorithms can incorporate advanced mathematical models that better capture the nuances of financial data, leading to more accurate risk assessments.

Enhanced Simulations: Quantum Monte Carlo methods can simulate market scenarios with greater efficiency, allowing for more thorough stress testing and scenario analysis.

3. Complex Optimization Techniques:

Quantum Approximate Optimization Algorithm (QAOA): This algorithm is specifically designed for combinatorial optimization problems, such as selecting the best portfolio. It helps find optimal asset allocations by exploring various combinations more efficiently.

Quantum Machine Learning: Integrating quantum machine learning techniques can improve predictive analytics, identifying trends and patterns that inform better investment strategies.

Practical Applications in Finance

Dynamic Asset Allocation: Quantum algorithms enable real-time adjustments to portfolio allocations based on market changes, enhancing responsiveness to volatility and risk.

Risk Diversification: By analyzing complex relationships between assets, quantum algorithms can optimize diversification strategies, reducing overall portfolio risk.

Algorithmic Trading: Quantum-enhanced trading algorithms can quickly identify and exploit arbitrage opportunities, optimizing execution strategies while managing risk.

Case Studies and Real-World Implementations

Hedge Funds and Asset Managers: Several firms are exploring quantum computing for portfolio optimization, utilizing quantum algorithms to enhance their risk management strategies and gain competitive advantages.

Financial Institutions: Major banks are investigating the integration of quantum computing to improve their risk assessment models and portfolio optimization processes.

Challenges and Considerations

While the potential of quantum algorithms in portfolio optimization is significant, several challenges remain:

Technology Readiness: Quantum computing is still developing, and many financial institutions may not have access to the necessary hardware or expertise.

Data Security: As quantum computing advances, concerns about data security and privacy also rise, necessitating robust protective measures.

Integration with Existing Systems: Implementing quantum algorithms within traditional financial systems requires careful planning and technical knowledge.

Conclusion

Quantum algorithms are set to revolutionize financial portfolio optimization and risk management. By harnessing the power of quantum computing, financial professionals can enhance their ability to assess risk, optimize asset allocations, and respond rapidly to market changes. As the technology matures, those who embrace quantum innovations will likely gain a competitive edge in the ever-evolving financial landscape, paving the way for more informed, effective investment strategies.

[dentalspecialistsgroup]

5 Cutting-Edge Dental Technologies Transforming Oral Health

Innovations in dental technology have revolutionized the field, enhancing patient experiences, treatment outcomes, and overall oral health. From advanced imaging systems to minimally invasive procedures, here are five cutting-edge dental technologies that are transforming the landscape of dentistry.

Digital Impressions and 3D Printing

Traditional dental impressions involving messy moulds and discomfort for patients are becoming obsolete with the advent of digital impression systems. These systems use intraoral scanners to capture highly accurate 3D images of patient's teeth and gums, eliminating the need for physical impressions. Not only do digital impressions provide greater precision, but they also offer a more comfortable experience for patients.

Furthermore, these digital scans can be seamlessly integrated with 3D printing technology to create precise, custom-made dental restorations such as crowns, bridges, and dentures. 3D printing enables dentists to produce prosthetics with exceptional accuracy and efficiency, reducing the turnaround time for patients and improving the overall quality of dental restorations.

Laser Dentistry

Laser technology has revolutionized various dental procedures, offering numerous benefits over traditional methods. Lasers are now commonly used in procedures such as cavity detection, gum disease treatment, and soft tissue surgeries. One of the key advantages of laser dentistry is its precision, allowing dentists to target specific areas with minimal damage to surrounding tissues.

Additionally, laser procedures often result in less discomfort, swelling, and bleeding compared to traditional techniques, leading to quicker recovery times for patients. With advancements in laser technology, dentists can now perform a wide range of procedures more efficiently and effectively, ultimately improving patient outcomes.

Augmented Reality (AR) in Dentistry

Augmented reality technology is making its way into the dental field, offering new possibilities for treatment planning, patient education, and even hands-on training for dental professionals. AR applications allow dentists to overlay digital images and information onto real-world views, providing enhanced visualization and understanding of complex dental concepts.

For patients, AR can be used to simulate treatment outcomes and demonstrate oral hygiene techniques in a more engaging and interactive manner. Dentists can also utilize AR during procedures to guide their actions with real-time feedback, ensuring greater precision and accuracy.

Teledentistry

The rise of telehealth has extended to dentistry with the emergence of teledentistry platforms, enabling patients to receive remote consultations and oral care services from the comfort of their homes. Through video conferencing and digital communication tools, dentists can assess oral health issues, provide guidance on preventive care, and even prescribe medications when necessary.

Teledentistry is particularly beneficial for patients in rural or underserved areas who may have limited access to dental care facilities. It also offers convenience and flexibility for busy individuals who struggle to schedule in-person appointments. By leveraging technology, teledentistry expands access to oral healthcare services while improving overall efficiency and convenience for both patients and providers.

Artificial Intelligence (AI) in Oral Diagnosis

Artificial intelligence is increasingly being integrated into dental practices to assist with oral diagnosis and treatment planning. AI algorithms analyze vast amounts of patient data, including medical history, diagnostic images, and treatment outcomes, to identify patterns and make predictive recommendations.

AI-powered diagnostic tools can aid in the early detection of oral diseases such as cavities, gum disease, and oral cancers, enabling timely intervention and improved treatment outcomes. Additionally, AI algorithms can help dentists optimize treatment plans by considering factors such as patient preferences, medical history, and risk factors.

Conclusion In:

conclusion, the rapid advancements in dental technology are revolutionizing the way oral healthcare is delivered and experienced. From digital impressions and laser dentistry to augmented reality and artificial intelligence, these cutting-edge technologies are enhancing diagnostic accuracy, treatment precision, and patient outcomes. Dentists who embrace these innovations stand to provide superior care and elevate the standard of oral health for their patients. Reach out to us today for more information.

[blogchaindeveloper]

How to Become an Artificial Intelligence Developer

Artificial intelligence has seen a significant rise in influence in technology in recent years. AI has unquestionably impacted everything, from the emergence of voice assistants like Siri and Alexa to the growth of interactive chat bots like ChatGPT. Indeed, analysts project that the worldwide artificial intelligence market might soar to an astounding value of more than $190 billion by 2025. 

With AI becoming increasingly prevalent in many different industries, there is an unprecedented need for qualified AI developers. Not only is this increase in demand a trend, but it also presents a favorable entry point for a wide range of fascinating career options.

We'll explain what AI certification is, what the duties and responsibilities of an AI developer are, and how to start this exciting career path in this extensive post. 

You're at the perfect place if you're an experienced professional wishing to make a career change in AI or an ambitious tech enthusiast. We'll go over the definition of AI certification, the value of AI developer certification, and how you may succeed in this rapidly evolving industry with the help of Blockchain Council's specialized AI prompt engineer certification. Let's start and open the door to a career in artificial intelligence development. 

What is the work of an AI developer? 

A specialist in the design, development, and upkeep of artificial intelligence systems and applications is called an AI developer or artificial intelligence developer. They aim to develop intelligent software capable of simulating cognitive processes, including learning, problem-solving, and decision-making. 

The Function and Accountabilities of an AI Developer 

An artificial intelligence developer's primary duties are creating and implementing AI solutions to solve business problems. Here is a summary of their primary responsibilities:

Algorithm Development: To enable machines to interpret data, identify patterns, and make judgments, AI engineers create and apply sophisticated algorithms. The foundation of AI applications is these algorithms. 

Machine Learning: They use many machine learning techniques, a branch of artificial intelligence, to create models that can learn from data and improve over time. Both supervised and unsupervised learning are popular methods. 

AI programmers specializing in natural language processing (NLP): Build machines that can comprehend and produce human language. Natural language processing is essential for chatbots, virtual assistants, and language translation apps. 

Computer Vision: AI developers use computer vision techniques to enable machines to understand visual data, identify objects, and even distinguish emotions on faces in applications like picture and video analysis. 

Deep Learning: Deep learning uses multi-layered neural networks, or "deep neural networks." AI developers use deep learning frameworks such as TensorFlow and PyTorch to construct and train these networks for various purposes. 

Data processing: AI cannot exist without data. Developers clean up and preprocess data to prepare it for AI model training. They may also be employed by big data technology. 

Competencies required to work as an AI developer 

A well-rounded skill set with technical and soft talents is needed to succeed as an AI developer. The essential competencies are broken down as follows: 

Knowledge of programming and software development: To be proficient in programming languages like Python, Java, C++, or R is essential. 

Knowledge of Software Engineering: It is essential to comprehend the fundamentals of software engineering. 

Critical thinking and problem-solving: Abilities are necessary for making well-informed decisions. 

Creativity: Developers must exercise creative thinking to develop innovative uses and solutions for AI technologies. Developing novel applications for AI is essential to its success. 

Attention to Detail: To find and fix errors, optimize algorithms, and guarantee the dependability of AI models, developers must pay acute attention to detail. 

Data science and machine learning: AI development requires a firm grasp of statistical analysis, data preprocessing, and machine learning techniques.

Deep Learning Structures: Neural networks and deep learning model development require familiarity with deep learning frameworks such as TensorFlow and PyTorch. 

How to Work as a Developer of Artificial Intelligence 

Blockchain Council offers AI developer and prompt engineer certifications to provide prospective AI developers with all the tools they need to succeed in this fast-paced industry. The courses offered by Blockchain Council cover programming languages, including Python, which is frequently used in the development of artificial intelligence. You'll obtain practical expertise in AI-specific software engineering techniques, guaranteeing your ability to build, create, and manage AI models efficiently. 

You will not only acquire the abilities required to become an Artificial Intelligence Developer by enrolling in Blockchain Council's AI courses and obtaining AI developer Certification, but you will also make a name for yourself as a skilled professional in the cutthroat AI industry. 

In summary 

Being an AI developer is an exciting and potential career choice, as artificial intelligence is increasing. The need for AI solutions is growing across industries, and competent AI engineers are in great demand. An AI developer's responsibilities include:

Developing AI systems.

Solving challenging business problems.

Programming.

Analyzing data.

Keeping up with new technological developments. 

The AI certification and exam from the Blockchain Council offer a strong basis and thorough training in all these crucial areas. Become a member of the Blockchain Council to gain the knowledge required to succeed as a certified chatbot specialist and participate in the AI revolution that will shape the future.

[aialgorithmicartuofw]

Week 14 - June 7 to June 13

Group 1

Group 2

GitHub - NVlabs/instant-ngp: Instant neural graphics primitives: lightning fast NeRF and more

https://github.com/NVlabs/instant-ngp GitHub - tetragonites/wolf3d: code four AI Alg Art project number 2code four AI Alg Art project number 2. Contribute to tetragonites/wolf3d development

https://github.com/tetragonites/wolf3d

https://github.com/users/tetragonites/projects/1

Group 3

Class Notes

How Sam Altman Stormed Washington to Set the A.I. Agenda.The chief executive of OpenAI, which makes ChatGPT, has met with at least 100 U.S. lawmakers in recent months. He has also taken his show abroad.

https://www.nytimes.com/2023/06/07/technology/sam-altman-ai-regulations.html

A Week With the Wild Children of the A.I. Boom

https://www.nytimes.com/2023/05/31/magazine/ai-start-up-accelerator-san-francisco.html

Google has created a Generative AI learning path with 9 FREE courses! Topics cover: - Intro to LLMs - Attention Mechanism - Image Generation/Captioning - Intro to Responsible AI From the fundamentals of LLMs to creating & deploying generative AI solutions! Read more Introduction to Generative AI: An introductory level micro-learning course aimed at explaining: - What Generative AI is - How it is used - How it differs from traditional ML Check this out https://lnkd.in/duKJ3sm6 Introduction to Large Language Models: The course explores: - Fundamentals LLMs - Their use cases - Prompt engineering on LLMs Check this out https://lnkd.in/dm_yS4MQ Introduction to Responsible AI: The course explains what responsible AI is, why it's important, and how Google implements responsible AI in their products. Check this out https://lnkd.in/dV8zNvwm Introduction to Image Generation: This course introduces diffusion models, a family of ML models that recently showed promise in the image generation space. Check this out https://lnkd.in/dqcZBZqd Encoder-Decoder Architecture: This course gives you a synopsis of the encoder-decoder architecture. It's a powerful and prevalent machine learning architecture for sequence-to-sequence tasks. Check this out https://lnkd.in/dhDhUgwJ Attention Mechanism: The course teaches you how attention works & how it revolutionised: - machine translation - text summarisation - question answering Check this out https://lnkd.in/dwsZZw6j Transformer Models and BERT Model: This course introduces you to some of the most famous and effective transformer architectures! Check this out https://lnkd.in/dK4p3n2s Create Image Captioning Models: This course teaches you how to create an image captioning model by using deep learning. Check this out https://lnkd.in/d8w32x5Y Introduction to Generative AI Studio: This course introduces Generative AI Studio, a product on Vertex AI. It teaches you to prototype and customize generative AI models so you can use their capabilities in your applications. Check this out https://lnkd.in/dAXdSrEXQwiklabsIntroduction to Generative AI | Google Cloud Skills BoostThis is an introductory level microlearning course aimed at explaining what Generative AI is, how it is used, and how it differs from traditional machine learning methods. It also covers Google Tools to help you develop your own Gen AI apps.This course is estimated to take approximately 45 minutes to complete.lnkd.inLinkedInThis link will take you to a page that’s not on LinkedInQwiklabsIntroduction to Responsible AI | Google Cloud Skills BoostThis is an introductory-level microlearning course aimed at explaining what responsible AI is, why it's important, and how Google implements responsible AI in their products. It also introduces Google's 7 AI principles.QwiklabsIntroduction to Image Generation | Google Cloud Skills BoostThis course introduces diffusion models, a family of machine learning models that recently showed promise in the image generation space. Diffusion models draw inspiration from physics, specifically thermodynamics. Within the last few years, diffusion models became popular in both research and industry. Diffusion models underpin many state-of-the-art image generation models and tools on Google Cloud. This course introduces you to the theory behind diffusion models and how to train and deploy them on Vertex AI.QwiklabsEncoder-Decoder Architecture | Google Cloud Skills BoostThis course gives you a synopsis of the encoder-decoder architecture, which is a powerful and prevalent machine learning architecture for sequence-to-sequence tasks such as machine translation, text summarization, and question answering. You learn about the main components of the encoder-decoder architecture and how to train and serve these models. In the corresponding lab walkthrough, you’ll code in TensorFlow a simple implementation of the encoder-decoder architecture for poetry generation from the beginning.

Audiocraft / MusicGen - AMAZING Text-To-Music AI Model By Facebook | Tutorial | Better Than MusicLM  https://www.youtube.com/watch?v=v-YpvPkhdO4

AlphaDev, an AI system using reinforcement learning to discover enhanced computer science algorithms.

https://twitter.com/DeepMind/status/1666462540367372291

What Happens When AI Enters the Concert Hall?

https://www.nytimes.com/2023/06/10/arts/music/ai-classical-music.html

From Thought to Text: AI Converts Silent Speech into Written Words - Neuroscience News

https://neurosciencenews.com/thoight-text-ai-decoder-23437/