AI Programming

Register for February's GDG Addis CodeNight Program CodeNight is a monthly developer program where coders build small interesting side projects. We gather every first week of the month as individuals or groups to build interesting projects during our spare time and present | demo them at the end of the week. 👥 CodeNight is open to individuals and groups of all skill level across the country 🇪🇹 Duration - 1 Week Orientation - Sunday, Feb 14 - 9:00 LT Demo day - Sunday, Feb 21 - 9:00 LT Format - Online event 📝 Register here - https://forms.gle/TGeAvQbKCQtm213q7 Join @CodeNight

#opportunityAlert Internship Opportunity! Global AI Hub is a Swiss-based leading global online community of AI Enthusiasts. A powerful platform providing both quality online AI education as well as AI career opportunities. We have an ambitious goal to make quality education accessible to all, bridge the gender divide, and help bring job opportunities to all those passionate and hard working. If this sounds like the kind of mission you would like to be a part of we are looking for interns! Simply fill out this form to submit an application. We look forward to working with you! Join us and became a shaper of your future. Apply Here: https://globalaihub.typeform.com/to/smebvec3

#opportunityAlert Internship Opportunity! Global AI Hub is a Swiss-based leading global online community of AI Enthusiasts. A powerful platform providing both quality online AI education as well as AI career opportunities. We have an ambitious goal to make quality education accessible to all, bridge the gender divide, and help bring job opportunities to all those passionate and hard working. If this sounds like the kind of mission you would like to be a part of we are looking for interns! Simply fill out this form to submit an application. We look forward to working with you! Join us and became a shaper of your future. Apply Here: https://globalaihub.typeform.com/to/smebvec3

Resources for Learning Python - - - - - - - - - - - - - - - - - - - - - - - - - - - Here are some resources that can help you get started learning how to code. 💢Python በ አማርኛ መማር ለምትፈልጉ 👉🏻 link ✅ Code Newbie Podcast ✅ Real python Podcast ✅ tutorialspoint 🔆Python for data scientist ✅ Data Analysis in Python ✅ free data science resources 🔅 Python for web ✅ Django Tutorial ✅ Python Web Dev 🔆 Best YouTube Video ✅ Python (በ አማርኛ) ✅ Python Mosh

Congratulations to Soliyana !! ————— AI Programmers team like to congratulate the 10 years old Ethiopian Soliyana Gizaw, she has been named the winner of the 2020 African Code Challenge (AfriCAN Code), a newly launched continent-wide tech competition where young people were invited to create an educational computer game. According to organizers Soliyana’s submission called “Mathstainement” won the Pan-African prize leading participants from 54 countries. ————— Inspire. Research. Create. 🙌🏽 AI Programming @freecodecs

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” Though most AI researchers acknowledge that large language models don’t actually understand language and are merely excellent at manipulating it, Big Tech can make money from models that manipulate language more accurately, so it keeps investing in them. “This research effort brings with it an opportunity cost,” Gebru and her colleagues write. Not as much effort goes into working on AI models that might achieve understanding, or that achieve good results with smaller, more carefully curated data sets (and thus also use less energy). Illusions of meaning The final problem with large language models, the researchers say, is that because they’re so good at mimicking real human language, it’s easy to use them to fool people. There have been a few high-profile cases, such as the college student who churned out AI-generated self-help and productivity advice on a blog, which went viral. The dangers are obvious: AI models could be used to generate misinformation about an election or the covid-19 pandemic, for instance. They can also go wrong inadvertently when used for machine translation. The researchers bring up an example: In 2017, Facebook mistranslated a Palestinian man’s post, which said “good morning” in Arabic, as “attack them” in Hebrew, leading to his arrest. Why it matters Gebru and Bender’s paper has six coauthors, four of whom are Google researchers. Bender asked to avoid disclosing their names for fear of repercussions. (Bender, by contrast, is a tenured professor: “I think this is underscoring the value of academic freedom,” she says.) The paper’s goal, Bender says, was to take stock of the landscape of current research in natural-language processing. “We are working at a scale where the people building the things can’t actually get their arms around the data,” she said. “And because the upsides are so obvious, it’s particularly important to step back and ask ourselves, what are the possible downsides? … How do we get the benefits of this while mitigating the risk?” In his internal email, Dean, the Google AI head, said one reason the paper “didn’t meet our bar” was that it “ignored too much relevant research.” Specifically, he said it didn’t mention more recent work on how to make large language models more energy efficient and mitigate problems of bias.  However, the six collaborators drew on a wide breadth of scholarship. The paper’s citation list, with 128 references, is notably long. “It’s the sort of work that no individual or even pair of authors can pull off,” Bender said. “It really required this collaboration.”  The version of the paper we saw does also nod to several research efforts on reducing the size and computational costs of large language models, and on measuring the embedded bias of models. It argues, however, that these efforts have not been enough. “I’m very open to seeing what other references we ought to be including,” Bender said. Nicolas Le Roux, a Google AI researcher in the Montreal office, later noted on Twitter that the reasoning in Dean’s email was unusual. “My submissions were always checked for disclosure of sensitive material, never for the quality of the literature review,” he said.

The paper The paper, which builds on the work of other researchers, presents the history of natural-language processing, an overview of four main risks of large language models, and suggestions for further research. Since the conflict with Google seems to be over the risks, we’ve focused on summarizing those here.  Environmental and financial costs Training large AI models consumes a lot of computer processing power, and hence a lot of electricity. Gebru and her coauthors refer to a 2019 paper from Emma Strubell and her collaborators on the carbon emissions and financial costs of large language models. It found that their energy consumption and carbon footprint have been exploding since 2017, as models have been fed more and more data.  Common carbon footprint benchmarks in lbs of CO2 equivalent Roundtrip flight b/w NY and SF (1 passenger) 1,984 Human life (avg. 1 year) 11,023 American life (avg. 1 year) 36,156 US car including fuel (avg. 1 lifetime) 126,000 Transformer (213M parameters) w/ neural architecture search Strubell’s study found that training one language model with a particular type of “neural architecture search” (NAS) method would have produced the equivalent of 626,155 pounds (284 metric tons) of carbon dioxide—about the lifetime output of five average American cars. Training a version of Google’s language model, BERT, which underpins the company’s search engine, produced 1,438 pounds of CO2 equivalent in Strubell’s estimate—nearly the same as a round-trip flight between New York City and San Francisco. These numbers should be viewed as minimums, the cost of training a model one time through. In practice, models are trained and retrained many times over during research and development. <iframe title="The estimated costs of training a model once" aria-label="chart" id="datawrapper-chart-wVVI7" src="https://datawrapper.dwcdn.net/wVVI7/6/" scrolling="no" frameborder="0" height="631" data-amp-original-style="width: 0; min-width: 100% !important; border: none;" class="amp-wp-372fa0f"></iframe> Gebru’s draft paper points out that the sheer resources required to build and sustain such large AI models means they tend to benefit wealthy organizations, while climate change hits marginalized communities hardest. “It is past time for researchers to prioritize energy efficiency and cost to reduce negative environmental impact and inequitable access to resources,” they write. Massive data, inscrutable models Large language models are also trained on exponentially increasing amounts of text. This means researchers have sought to collect all the data they can from the internet, so there’s a risk that racist, sexist, and otherwise abusive language ends up in the training data. An AI model taught to view racist language as normal is obviously bad. The researchers, though, point out a couple of more subtle problems. One is that shifts in language play an important role in social change; the MeToo and Black Lives Matter movements, for example, have tried to establish a new anti-sexist and anti-racist vocabulary. An AI model trained on vast swaths of the internet won’t be attuned to the nuances of this vocabulary and won’t produce or interpret language in line with these new cultural norms. It will also fail to capture the language and the norms of countries and peoples that have less access to the internet and thus a smaller linguistic footprint online. The result is that AI-generated language will be homogenized, reflecting the practices of the richest countries and communities. Moreover, because the training data sets are so large, it’s hard to audit them to check for these embedded biases. “A methodology that relies on datasets too large to document is therefore inherently risky,” the researchers conclude. “While documentation allows for potential accountability, […] undocumented training data perpetuates harm without recourse.” Research opportunity costs The researchers summarize the third challenge as the risk of “misdirected research effort.

𝗪𝗘𝗕 𝗗𝗘𝗩𝗘𝗟𝗢𝗣𝗘𝗥 𝗥𝗢𝗔𝗗𝗠𝗔𝗣 - - - - - - - - - - - - - - - - - - - - - - - - 🌀2 ወይም 3 𝗪𝗲𝗯𝘀𝗶𝘁𝗲 ከሰራን በዋላ💪🏽 𝗜 𝗮𝗺 𝗮 𝗳𝘂𝗹𝗹 𝘀𝘁𝗮𝗰𝗸 𝘄𝗲𝗯 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿 ለምንል ሰዎች የተዘጋጀ⁉️ A𝗽𝗽 ወይም 𝘄𝗲𝗯𝘀𝗶𝘁𝗲 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿 ለመሆን ምን ያስፈልጋል ፣ ምን 𝗽𝗿𝗼𝗴𝗿𝗮𝗺𝗺𝗶𝗻𝗴 𝗹𝗮𝗻𝗴𝘂𝗮𝗴𝗲 ልጠቅም፤ ምን ልማር የመሳሰሉትን ጥያቄዎች⁉️ መልስ 💢𝗧𝗵𝗲𝘀𝗲 𝗿𝗼𝗮𝗱𝗺𝗮𝗽𝘀 𝗰𝗼𝘃𝗲𝗿 𝗲𝘃𝗲𝗿𝘆𝘁𝗵𝗶𝗻𝗴 𝘁𝗵𝗮𝘁 𝗶𝘀 𝘁𝗵𝗲𝗿𝗲 𝘁𝗼 𝗹𝗲𝗮𝗿𝗻 𝗳𝗼𝗿 𝘁𝗵𝗲 𝗽𝗮𝘁𝗵𝘀 𝗹𝗶𝘀𝘁𝗲𝗱 𝗯𝗲𝗹𝗼𝘄. 𝗗𝗼𝗻'𝘁 𝗳𝗲𝗲𝗹 𝗼𝘃𝗲𝗿𝘄𝗵𝗲𝗹𝗺𝗲𝗱, 𝘆𝗼𝘂 𝗱𝗼𝗻'𝘁 𝗻𝗲𝗲𝗱 𝘁𝗼 𝗹𝗲𝗮𝗿𝗻 𝗶𝘁 𝗮𝗹𝗹 𝗶𝗻 𝘁𝗵𝗲 𝗯𝗲𝗴𝗶𝗻𝗻𝗶𝗻𝗴 𝗶𝗳 𝘆𝗼𝘂 𝗮𝗿𝗲 𝗷𝘂𝘀𝘁 𝗴𝗲𝘁𝘁𝗶𝗻𝗴 𝘀𝘁𝗮𝗿𝘁𝗲𝗱.💢 https://github.com/kamranahmedse/developer-roadmap

መልካም ከተራ እና ጥምቀት! Happy Epiphany!

Hello GDG Addis family, We are completely moving from Meetup to the new platform by the next 2 weeks. In order to take advantage of our upcoming programs, please join to our new home using the link below: https://gdg.community.dev/gdg-addis/ @GDGAddis

"ቤዛ ኩሉ ዓለም ዮም ተወልደ" ቅዱስ ያሬድ መልካም ገና! ________________________ AI Programming @freecodecs

Server Side Dynamic Web Development • ዳታቤዝ ያላቸው ድህረገጾች አሰራር • Part/ክፍል 2 _____ በዚህ ክፍል server side dynamic application development ወይም ማበልጸጊያዎች ላይ ለመስራት የሚያስፈልጉ Platforms አጫጫን እና አጠቃቀማቸውን በጥልቀት እንመለከታለን:: ቻናላችንን በመቀላቀል እና ለወዳጅዎ በማጋራት ሌሎች ተመሳሳይ የቪዲዮ ትምህርቶች እንዲደርስዎ ያድርጉ:: https://youtube.com/watch?v=yIRo0_RhBeg&feature=share

Python Programming (Running Code) • ፓይተን ፕሮግራም አጻጻፍና አከፋፈት • (Part/ክፍል 2) ዛሬ የፓይተን ፕሮግራሞችን እንዴት እንደምንከፍት እና ጽፈን Run እንደምናረግ በ3 መንገዶች እናያለን። ይህን ቪዲዮ ለመከታተል እንዲያመቻቹ የባለፈውን ክፍል ማየት አለባቹ እሱን ለማየት ኮርነር ላይ ወይም Description ስር ሊንኩን አስቀምጠንላቹኋል። ለUnix ተጠቃሚዎች ደግሞ እሱን የዚ ቪዲዮ መጨረሻ Chapter ላይ በደቂቃ Navigator ቀጥታ መሄድ እና ማየት ትችላላቹ። https://youtube.com/watch?v=47mmb6Rkn_Y&feature=share ____________ AI Programming @freecodecs Spread the Knowledge!

Python Programming • ፓይተን ፕሮግራሚንግ • Part/ክፍል 1 (Installing / አጫጫን) _____ በዚህ ቪዲዮ ላይ ፓይተን ፕሮግራም ለመጠቀም የሚያስችሉ ሶፍትዌሮችን አጫጫን እና ስለ ፓይተን ለመግቢያ እንዲሆናቹ እናብራራላቹሃለን። https://youtube.com/watch?v=DyE4LB3_tU8&feature=share