Two weeks into the coding class he was teaching at Duke University in North Carolina this spring, Noah Gift told his students to throw out the course materials he’d given them. Instead of working with Python, one of the most popular entry-level programming languages, the students would now be using Rust, a language that was newer, more powerful, and much harder to learn.
Gift, a software developer with 25 years of experience, had only just learned Rust himself. But he was confident his students would be fine with the last-minute switch-up. That’s because they’d also each get a special new sidekick: an AI tool called Copilot, a turbocharged autocomplete for computer code, built on top of OpenAI’s latest large language models, GPT-3.5 and GPT-4.
Copilot is made by GitHub, a firm that runs an online software development platform used by more than 100 million programmers. The tool monitors every keystroke you make, predicts what you are trying to do on the fly, and offers up a nonstop stream of code snippets you could use to do it. Gift, who had been told about Copilot by someone he knew at GitHub’s parent company, Microsoft, saw its potential at once.
“There’s no way I could have learned Rust as quickly as I did without Copilot,” he says. “I basically had a supersmart assistant next to me that could answer my questions while I tried to level up. It was pretty obvious to me that we should start using it in class.”
Gift isn't alone. Ask a room of computer science students or programmers if they use Copilot, and many now raise a hand. All the people interviewed for this article said they used Copilot themselves—even those who pointed out problems with the tool.
Like ChatGPT with education, Copilot is upending an entire profession by giving people new ways to perform old tasks. Packaged as a paid-for plug-in for Microsoft’s Visual Studio software (a kind of industry-standard multi-tool for writing, debugging, and deploying code), Copilot is the slickest version of this tech. But it’s not the only tool available to coders. In August, Meta released a free code-generation model called Code Llama, based on Llama 2, Meta’s answer to GPT-4. The same month, Stability AI—the firm behind the image-making model Stable Diffusion—put out StableCode. And, of course, there’s ChatGPT, which OpenAI has pitched from the start as a chatbot that can help write and debug code.
“It’s the first time that machine-learning models have been really useful for a lot of people,” says Gabriel Synnaeve, who led the team behind Code Llama at Meta. “It’s not just nerding out—it’s actually useful.”
With Microsoft and Google about to stir similar generative models into office software used by billions around the world (Microsoft has started using Copilot as a brand name across Office 365), it’s worth asking exactly what these tools do for programmers. How are they changing the basics of a decades-old job? Will they help programmers make more and better software? Or will they get bogged down in legal fights over IP and copyright?
Cranking out code
On the surface, writing code involves typing statements and instructions in some programming language into a text file. This then gets translated into machine code that a computer can run—a level up from the 0s and 1s of binary. In practice, programmers also spend a lot of time googling, looking up workarounds for common problems or skimming online forums for faster ways to write an algorithm. Existing chunks of prewritten code then get repurposed, and new software often comes together like a collage.
But these look-ups take time and let programmers out of the flow of converting thoughts into code, says Thomas Dohmke, GitHub’s CEO: “You’ve got a lot of tabs open, you’re planning a vacation, maybe you’re reading the news. At last you copy the text you need and go back to your code, but it’s 20 minutes later and you lost the flow.”
The key idea behind Copilot and other programs like it, sometimes called code assistants, is to put the information that programmers need right next to the code they are writing. The tool tracks the code and comments (descriptions or notes written in natural language) in the file that a programmer is working on, as well as other files that it links to or that have been edited in the same project, and sends all this text to the large language model behind Copilot as a prompt. (GitHub co-developed Copilot's model, called Codex, with OpenAI. It is a large language model fine-tuned on code.) Copilot then predicts what the programmer is trying to do and suggests code to do it.
This round trip between code and Codex happens multiple times a second, the prompt updating as the programmer types. At any moment, the programmer can accept what Copilot suggests by hitting the tab key, or ignore it and carry on typing.
The tab button seems to get hit a lot. A study of almost a million Copilot users published by GitHub and the consulting firm Keystone Strategy in June—a year after the tool’s general release—found that programmers accepted on average around 30% of its suggestions, according to GitHub’s user data.
“In the last year Copilot has suggested—and had okayed by developers—more than a billion lines of code,” says Dohmke. “Out there, running inside computers, is code generated by a stochastic parrot.”
Copilot has changed the basic skills of coding. As with ChatGPT or image makers like Stable Diffusion, the tool’s output is often not exactly what’s wanted—but it can be close. “Maybe it’s correct, maybe it’s not—but it’s a good start,” says Arghavan Moradi Dakhel, a researcher at Polytechnique Montréal in Canada who studies the use of machine-learning tools in software development. Programming becomes prompting: rather than coming up with code from scratch, the work involves tweaking half-formed code and nudging a large language model to produce something more on point.
But Copilot isn’t everywhere yet. Some firms, including Apple, have asked employees not to use it, wary of leaking IP and other private data to competitors. For Justin Gottschlich, CEO of Merly, a startup that uses AI to analyze code across large software projects, that will always be a deal-breaker: “If I’m Google or Intel and my IP is my source code, I’m never going to use it,” he says. “Why don’t I just send you all my trade secrets too? It’s just put-your-pants-on-before-you-leave-the-house kind of obvious.” Dohmke is aware this is a turn-off for key customers and says that the firm is working on a version of Copilot that businesses can run in-house, so that code isn’t sent to Microsoft’s servers.
Copilot is also at the center of a lawsuit filed by programmers unhappy that their code was used to train the models behind it without their consent. Microsoft has offered indemnity to users of its models who are wary of potential litigation. But the legal issues will take years to play out in the courts.
Dohmke is bullish, confident that the pros outweigh the cons: “We will adjust to whatever US, UK, or European lawmakers tell us to do,” he says. “But there is a middle balance here between protecting rights—and protecting privacy—and us as humanity making a step forward.” That’s the kind of fighting talk you’d expect from a CEO. But this is new, uncharted territory. If nothing else, GitHub is leading a brazen experiment that could pave the way for a wider range of AI-powered professional assistants.
GitHub started working on Copilot in June 2020, soon after OpenAI released GPT-3. Programmers have always been on the lookout for shortcuts and speedups. “It’s part of the DNA of being a software developer,” says Dohmke. “We wanted to solve this problem of boilerplate code—can we generate code that is no fun to write but takes up time?”
The first sign they were onto something came when they asked programmers at the company to submit coding tests that they might ask somebody at a job interview: “Here’s some code—finish it off.” GitHub gave these to an early version of the tool and let it try each test 150 times. Given that many attempts, they found that the tool could solve 92% of them. They tried again with 50,000 problems taken from GitHub’s online platform, and the tool solved just over half of them. “That gave us confidence that we could build what ultimately became Copilot,” says Dohmke.
In 2023, a team of GitHub and Microsoft researchers tested the impact of Copilot on programmers in a small study. They asked 95 people to build a web server (a non-trivial task, but one involving the kind of common, boilerplate code that Dohmke refers to) and gave half access to Copilot. Those using Copilot completed the task on average 55% faster.
A powerful AI that replaces the need for googling is useful—but is it a game changer? Opinion is split.
“The way that I would think about it is that you have an experienced developer sitting next to you whispering recommendations,” says Marco Iansiti, a Keystone Strategy cofounder and a professor at Harvard Business School, where he studies digital transformation. “You used to have to look things up on your own, and now—whammo—here comes the suggestion automatically.”
Gottschlich, who has been working on automatic code generation for years, is less impressed. “To be frank, code assistants are fairly uninteresting in the larger scheme of things,” he says, referring to the new wave of tools based on large language models, like Copilot. “They are principally bound by what the human programmer is capable of doing. They’ll never likely at this stage be able to do something miraculous beyond what the human programmer is doing.”
Gottschlich, who claims that Merly’s tech finds bugs in code and fixes them by itself (but who doesn’t shed light on how that works), is thinking bigger. He sees AI one day taking on the management of vast and complex libraries of code, directing human engineers in how to maintain it. But he doesn’t think large language models are the right tech for that job.
Even so, small changes to a task that millions of people do all the time can add up fast. Iansiti, for example, makes a huge claim: he believes that the impact of Copilot—and tools like it—could add $1.5 trillion to the global economy by 2030. “It’s more of a back-of-the-envelope thing, not really an academic estimate, but it could be a lot larger than that as well,” he says. “There’s so much stuff that hinges on software. If you move the needle on how software development really works, it will have an infinite impact on the economy.”
For Iansiti, it’s not just about getting existing developers to produce more code. He argues that these tools will increase the demand for programmers because companies could get more code for less money. At the same time, there will be more coders because these tools lower the barrier to entry. “We’re going to see an expansion in who can contribute to software development,” he says.
Or as Idan Gazit, GitHub’s senior director of research, puts it: imagine if anyone who picked up a guitar could play a basic tune straight away. There would be a lot more guitar players and a lot more music.
Many agree that Copilot makes it easier to pick up programming—as Gift found. “Rust has got a reputation for being a very difficult language,” he says. “But I was pleasantly shocked at how well the students did and at the projects they built—how complex and useful they were.” Gift says they were able to build complete web apps with chatbots in them.
Not everyone was happy with Gift’s syllabus change, however. He says that one of his teaching assistants told new students not to use Copilot because it was a crutch that would stop them from learning properly. Gift accepts that Copilot is like training wheels that you might not want to take off. But he doesn’t think that’s a problem: “What are we trying to do? We’re trying to build complex systems.” And to do that, he argues, programmers should use whatever tools are available.
It is true that the history of computing has seen programmers rely on more and more layers of software between themselves and the machine code that computers can run. They have gone from punch cards and assembly code to programming languages like Python that are relatively easy to read and write. That’s possible because such languages get translated into machine code by software called compilers. “When I started coding in the ’80s and ’90s, you still had to know how a CPU worked,” says Dohmke. “Now when you write a web application, you almost never think about the CPU or the web server.”
Add in a long list of bug-catching and code-testing tools, and programmers are used to a large amount of automated support. In many ways, Copilot and others are just the latest wave. “I used Python for 25 years because it was written to be readable by humans,” says Gift. “In my opinion, that doesn’t matter anymore.”
But he points out that Copilot isn’t a free pass. “Copilot reflects your ability,” he says. “It lifts everyone up a little bit, but if you’re a poor programmer you’ll still have weaknesses.”
Work to be done
A big problem with assessing the true impact of such tools is that most of the data is still anecdotal. GitHub’s study showed that programmers were accepting 30% of suggestions (“30% is out of this world in any kind of industry scenario,” says Dohmke), but it is not clear why the programmers accepted those suggestions and rejected others.
The same study also revealed that less experienced programmers accepted more suggestions and that programmers accepted more suggestions as they grew used to the tool—but, again, not why. “We need to go a lot deeper to understand what that means,” says Iansiti. “There’s work to be done to really get a sense of how the coding process itself is developing, and that work is all TBD.”
Most independent studies of tools like Copilot have focused on the correctness of the code that they suggest. Like all large language models, these tools can produce nonsense. With code it can be hard to tell—especially for less experienced users, who also seem to rely on Copilot the most.
Several teams of researchers in the last couple of years have found that Copilot can insert bugs or security flaws into code. GitHub has been busy improving the accuracy of Copilot’s suggestions. It claims that the latest version of the tool runs code through a second model trained to filter out common security bugs before making a suggestion to users.
But there are other quality issues beyond bugs, says Dakhel. She and her colleagues have found that Copilot can suggest code that is overly complex or doesn’t adhere to what professionals consider best practices, which is a problem because complex or unclear code is harder for other people to read, check, and extend.
The problem is that models are only as good as their training data. And Copilot’s models were trained on a vast library of code taken from GitHub’s online repository, which goes back 15 years. This code contains not only bugs but also security flaws that were not known about when the code was written.
Add to this the fact that inexperienced programmers use the tool more than experienced ones, and it could make more work for software development teams in the long run, says Dakhel. Expert programmers may have to spend more time double-checking the code put through by non-experts.
Dakhel now hopes to study the gap between expert and non-expert programmers more fully. Before Copilot was released, she and her colleagues were using machine learning to detect expert programmers by their code. But Copilot messed with her data because now it was harder to tell whether code had been written by an expert programmer or a less experienced one with AI help.
Now, having played around with Copilot herself, she plans to use her approach to study what kind of boost it gives. “I’m curious to know if junior developers using such a tool will be predicted to be expert developers or if it’s still detectable that they are junior developers,” she says. “It could be a way of measuring how big a level up these tools give people."
Ultimately, we might not have to wait long before the jury is in. Software development is one of the most well documented and thoroughly measured of business activities. If Copilot works, it will get used. If it doesn’t, it won’t. In the meantime, these tools are getting better all the time.
Yet it is worth noting that programming—typing text onto a screen—is a small part of the overall job of software development. It involves managing multiple parts of a complex puzzle, including designing the code, testing it, and deploying it. Copilot, like many programs before it, can make parts of that job faster, but it won’t reinvent it completely.
“There’s always going to be programmers,” says Synnaeve. “They will get a lot of help, but in the end what matters is understanding which problems need solving. To do that really well and translate that into a program—that’s the job of programmers.”
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Majuma mawili baada ya kuanza darasa la programu alilokuwa akifundisha katika Chuo Kikuu cha Duke huko North Carolina spring hii, Noah Gift aliwaambia wanafunzi wake kutupa vifaa vya kozi alivyokuwa amewapa. Badala ya kutumia Python, mojawapo ya lugha maarufu zaidi za programu za msingi, wanafunzi sasa wangetumia Rust, lugha mpya, yenye nguvu zaidi, na ngumu zaidi kujifunza. Gift, msanidi programu mwenye uzoefu wa miaka 25, alikuwa amejifunza Rust mwenyewe. Lakini alikuwa na uhakika kwamba wanafunzi wake wangekubali kubadilishwa kwa dakika ya mwisho. Hiyo ni kwa sababu wao pia kila kupata maalum mpya sidekick: AI chombo aitwaye Copilot, turbocharged autocomplete kwa ajili ya kanuni ya kompyuta, kujengwa juu ya OpenAI ya karibuni kubwa lugha mifano, GPT-3.5 na GPT-4. Copilot ni kampuni ya GitHub, ambayo inaendesha jukwaa la maendeleo ya programu ya mtandaoni inayotumiwa na zaidi ya programu milioni 100. Programu hii inasimamia kila kitufe unachokiandika, inaweka utabiri wa kile unachojaribu kufanya, na hutoa mtiririko wa vipande vya nambari ambavyo unaweza kutumia kufanya hivyo. Gift, ambaye alikuwa ameambiwa kuhusu Copilot na mtu aliyemjua katika kampuni ya mama ya GitHub, Microsoft, aliona uwezo wake mara moja. "Hakuna njia ningeweza kujifunza Rust haraka kama nilivyofanya bila Copilot", anasema. <unk>Kimsingi nilikuwa na msaidizi wa akili sana kando yangu ambaye angeweza kujibu maswali yangu wakati nilijaribu kupanda ngazi. Ilikuwa wazi sana kwangu kwamba tunapaswa kuanza kuitumia darasani. <unk> Zawadi si peke yake. "Kama unauliza wanafunzi wa sayansi ya kompyuta au wa programu kama wanatumia ""Copilot,"" wengi sasa kuinua mkono." Watu wote waliohojiwa kwa ajili ya makala hii alisema walitumia Copilot wenyewe - hata wale ambao walionyesha matatizo na chombo. Kama ChatGPT na elimu, Copilot ni upending taaluma nzima kwa kuwapa watu njia mpya ya kufanya kazi za zamani. Packaged kama kulipwa kwa ajili ya plug-katika kwa Microsoft Visual Studio programu (a aina ya sekta ya kiwango cha multi-chombo kwa ajili ya kuandika, debugging, na kupeleka code), Copilot ni toleo slickest ya teknolojia hii. Lakini si chombo pekee inapatikana kwa coders. Katika Agosti, Meta ilitoa bure code-kizazi mfano aitwaye Code Llama, kulingana na Llama 2, Meta jibu kwa GPT-4. Mwezi huo huo, Uimarishaji AI - kampuni nyuma ya mfano wa kutengeneza picha ya Kuenea kwa Uimarishaji - ilitoa StableCode. Na, bila shaka, kuna ChatGPT, ambayo OpenAI imeweka tangu mwanzo kama chatbot ambayo inaweza kusaidia kuandika na debug code. "Ni mara ya kwanza kwamba mifano ya kujifunza mashine imekuwa kweli muhimu kwa watu wengi", anasema Gabriel Synnaeve, ambaye aliongoza timu nyuma ya Code Llama katika Meta. "Kwa kuwa Microsoft na Google ziko karibu kuunganisha mifano kama hiyo ya kizazi katika programu ya ofisi inayotumiwa na mabilioni ulimwenguni kote (Microsoft imeanza kutumia Copilot kama jina la chapa katika Ofisi ya 366), inafaa kuuliza ni nini hasa zana hizi hufanya kwa waandaaji wa programu.""" Je, ni nini kinachobadilisha kazi ya miaka mingi? Je, watawasaidia watengenezaji wa programu kutengeneza programu nyingi na bora zaidi? Au watashindwa katika mapambano ya kisheria juu ya IP na hakimiliki? Kuandika code ni kuandika taarifa na maelekezo katika lugha fulani ya programu katika faili ya maandishi. Hii kisha hupatikana kutafsiriwa katika code mashine kwamba kompyuta inaweza kukimbia <unk> ngazi juu kutoka 0s na 1s ya binary. Katika mazoezi, watengenezaji wa programu pia hutumia muda mwingi kutafiti, kutafuta njia za kutatua matatizo ya kawaida au kuchunguza vikao vya mtandaoni kwa njia za haraka za kuandika algorithm. Vipande vilivyopo vya nambari iliyoandikwa mapema kisha hutumiwa tena, na programu mpya mara nyingi huja pamoja kama collage. Lakini hizi kuangalia-ups kuchukua muda na basi watengenezaji wa programu nje ya mtiririko wa kugeuza mawazo katika code, anasema Thomas Dohmke, GitHub CEO: <unk>Wewe <unk>ve got mengi ya tabo wazi, wewe <unk>re mipango ya likizo, labda wewe <unk>re kusoma habari. Mwishowe unakopa maandishi unayohitaji na kurudi kwenye nambari yako, lakini ni dakika 20 baadaye na umepoteza mtiririko. wazo kuu nyuma ya Copilot na programu nyingine kama hiyo, wakati mwingine huitwa wasaidizi wa nambari, ni kuweka habari ambayo waandaaji wanahitaji karibu na nambari wanayoandika. Programu hiyo hufuatilia nambari na maelezo (maelezo au maelezo yaliyoandikwa kwa lugha ya asili) katika faili ambayo programu inafanya kazi, pamoja na faili nyingine ambazo zinaunganishwa au ambazo zimerekebishwa katika mradi huo huo, na hutuma maandishi haya yote kwa mfano mkubwa wa lugha nyuma ya Copilot kama mwongozo. (GitHub co-kuendeleza mfano Copilot ya, aitwaye Codex, na OpenAI. Ni kubwa lugha ya mfano finely tuned juu ya kificho.) Copilot kisha hutabiri nini programu ni kujaribu kufanya na inapendekeza code kufanya hivyo. Hii safari ya pande zote kati ya kanuni na Codex hutokea mara nyingi kwa sekunde, haraka kusasisha kama programu aina. Wakati wowote, programu inaweza kukubali nini Copilot inapendekeza kwa kugonga kitufe tab, au kupuuza yake na kuendelea kuandika. Bonyeza tab inaonekana kupata hit mengi. Utafiti wa karibu watumiaji milioni moja wa Copilot uliochapishwa na GitHub na kampuni ya ushauri ya Keystone Strategy mnamo Juni - mwaka mmoja baada ya kutolewa kwa chombo hicho - uligundua kuwa waandaaji wa programu walikubali wastani wa karibu 30% ya mapendekezo yake, kulingana na data ya watumiaji wa GitHub. "Katika mwaka uliopita Copilot imependekeza" na ilikuwa imeidhinishwa na watengenezaji "zaidi ya mistari bilioni ya kificho", anasema Dohmke. <unk>Huko nje, mbio ndani ya kompyuta, ni kanuni zinazozalishwa na stochastic parrot.<unk> Copilot imebadilisha ujuzi wa msingi wa coding. Kama na ChatGPT au watengenezaji wa picha kama Stable Diffusion, pato la chombo mara nyingi si hasa kile kinachotakiwa lakini inaweza kuwa karibu. "Labda ni sahihi, labda si hivyo, lakini ni mwanzo mzuri", anasema Arghavan Moradi Dakhel, mtafiti katika Polytechnique Montréal nchini Canada ambaye anachunguza matumizi ya zana za kujifunza mashine katika maendeleo ya programu. Programu inakuwa ya kuchochea: badala ya kuja na kificho kutoka mwanzo, kazi inahusisha kurekebisha kificho nusu-kuundwa na kusukuma mfano mkubwa wa lugha kuzalisha kitu zaidi juu ya uhakika. Lakini Copilot si kila mahali bado. Kampuni kadhaa, ikiwa ni pamoja na Apple, zimewaomba wafanyikazi wao wasitumie, wakitilia shaka kuvuja kwa IP na data nyingine za kibinafsi kwa washindani. "Justin Gottschlich, Mkurugenzi Mtendaji wa Merly, mwanzilishi wa AI, anasema: ""Ikiwa mimi ni Google au Intel na IP yangu ni chanzo changu, mimi kamwe sitatumia.""" <unk>Kwa nini si mimi tu kutuma wewe siri yangu yote ya biashara pia? "Hii ni ""kuweka-pants-yako-juu-kabla ya-wewe-kuondoka-nyumba""-a aina ya dhahiri. ""Dohmke anajua hii ni kugeuka-off kwa wateja muhimu na anasema kwamba kampuni ni kazi juu ya toleo la Copilot kwamba biashara inaweza kukimbia ndani ya nyumba, hivyo kwamba code si kutumwa kwa Microsoft ""servers." Copilot pia ni katikati ya kesi iliyowasilishwa na waandaaji wa programu wasioridhika kwamba nambari yao ilitumiwa kufundisha mifano nyuma yake bila idhini yao. Microsoft imetoa fidia kwa watumiaji wa mifano yake ambao ni tahadhari ya kesi ya uwezekano. Lakini masuala ya kisheria yatachukua miaka mingi ili kusuluhishwa mahakamani. Dohmke ni bullish, na uhakika kwamba faida ni kubwa kuliko hasara: "Tutajipatanisha na chochote Marekani, Uingereza, au wabunge wa Ulaya kutuambia kufanya", anasema. "Lakini kuna usawa wa kati hapa kati ya kulinda haki" na kulinda faragha "na sisi kama ubinadamu kufanya hatua mbele. " Hiyo ni aina ya mazungumzo ya mapigano unatarajia kutoka kwa Mkurugenzi Mtendaji". Lakini hii ni eneo jipya, lisilojulikana. GitHub inaongoza majaribio ya ujasiri ambayo yanaweza kuunda njia ya mfululizo mkubwa wa wasaidizi wa kitaaluma wa AI. GitHub ilianza kufanya kazi kwenye Copilot mnamo Juni 2020, muda mfupi baada ya OpenAI kutolewa GPT-3. Watengenezaji wa programu sikuzote wamekuwa wakitafuta njia za mkato na kuongeza mwendo. "Ni sehemu ya DNA ya kuwa msanidi programu", anasema Dohmke. "Kama kuna kitu ambacho kinaweza kutusaidia kuunda code ya kawaida, lakini kinachukua muda, ishara ya kwanza ya kuwa na kitu fulani ilikuja wakati waliwaomba waandaaji wa programu katika kampuni kuwasilisha vipimo vya coding ambavyo wangeweza kuuliza mtu kwenye mahojiano ya kazi: ""Hapa kuna baadhi ya code - kumaliza.""" Kwa sababu ya majaribio mengi, walipata kwamba chombo hicho kiliweza kutatua 92% ya maswali hayo. Kwa mfano, katika kesi ya GitHub, kuna matatizo 50,000 ambayo yanaweza kuondolewa na zana ya GitHub, na kwa kweli, ni rahisi zaidi. "Hiyo ilitupa ujasiri kwamba tunaweza kujenga kile ambacho hatimaye kilikuja kuwa Copilot", anasema Dohmke. Katika mwaka wa 2020, timu ya watafiti wa GitHub na Microsoft ilijaribu athari za Copilot kwa watengenezaji wa programu katika utafiti mdogo. "Waliwaomba watu 95 kujenga seva ya wavuti (kazi isiyo ya kawaida, lakini moja inayohusisha aina ya kawaida, ""boilerplate code"" ambayo Dohmke inahusu) na kutoa nusu ya upatikanaji wa Copilot." Kwa mfano, wale waliotumia programu ya Copilot walifanya kazi hiyo kwa kasi zaidi kwa asilimia 55. AI yenye nguvu ambayo inachukua nafasi ya haja ya googling ni muhimu <unk> lakini ni kubadilisha mchezo? Maoni yamegawanyika. "Ninaweza kufikiria juu ya hilo kwa njia ya kuwa na msanidi programu mwenye uzoefu ameketi kando yako akipiga kelele mapendekezo, ""anasema Marco Iansiti, mwanzilishi mwenza wa Mkakati wa Keystone na profesa katika Shule ya Biashara ya Harvard, ambapo anajifunza mabadiliko ya dijiti." <unk>Ulikuwa na kuangalia mambo juu ya yako mwenyewe, na sasa<unk>whammo<unk>hapa inakuja pendekezo moja kwa moja.<unk> Gottschlich, ambaye amekuwa kufanya kazi juu ya kizazi cha code moja kwa moja kwa miaka, ni chini ya hisia. "Kuwa wazi, wasaidizi wa kanuni ni pretty uninteresting katika mpango mkubwa wa mambo", anasema, akirejelea wimbi jipya la zana kulingana na mifano kubwa ya lugha, kama Copilot. <unk>Wao ni hasa amefungwa na nini programu ya binadamu ni uwezo wa kufanya. Wao kamwe uwezekano katika hatua hii kuwa na uwezo wa kufanya kitu ajabu zaidi ya kile programu ya binadamu ni kufanya. Gottschlich, ambaye anadai kwamba Merly ya teknolojia hupata bugs katika code na fixes yao yenyewe (lakini ambaye haina kutupa mwanga juu ya jinsi hiyo kazi), ni kufikiri kubwa. Yeye anaona AI siku moja kuchukua juu ya usimamizi wa maktaba kubwa na tata ya code, kuelekeza wahandisi binadamu katika jinsi ya kudumisha yake. Lakini yeye hafikiri mifano kubwa ya lugha ni teknolojia sahihi kwa ajili ya kazi hiyo. Hata hivyo, mabadiliko madogo-madogo katika kazi ambayo mamilioni ya watu hufanya wakati wote yanaweza kuongezeka upesi. "Mwanamke huyo wa pili, Ian Siti, amesema kuwa ""Copilot na vifaa kama hivyo vinaweza kuongeza dola trilioni 1.5 kwa uchumi wa dunia kufikia 2030.""" "Ni zaidi ya nyuma ya bahasha kitu, si kweli makadirio ya kitaaluma, lakini inaweza kuwa kubwa sana kuliko hiyo pia", anasema. <unk>Kuna mambo mengi sana ambayo hutegemea programu. Kama wewe kuhamisha sindano juu ya jinsi maendeleo ya programu kweli kazi, itakuwa na athari infinite juu ya uchumi. <unk> Kwa Iansiti, si tu kuhusu kupata watengenezaji zilizopo kuzalisha code zaidi. Yeye anadai kwamba zana hizi itaongeza mahitaji ya waandaaji wa programu kwa sababu makampuni inaweza kupata code zaidi kwa fedha kidogo. Wakati huo huo, kutakuwa na coders zaidi kwa sababu zana hizi kupunguza kizuizi cha kuingia. "Tutaona upanuzi katika nani anaweza kuchangia maendeleo ya programu", anasema. "Idan Gazit, mkurugenzi wa utafiti wa GitHub, alisema: ""Hebu wazia kama mtu yeyote ambaye alichukua gitaa angeweza kucheza wimbo wa msingi mara moja." Kutakuwa na wachezaji wengi zaidi wa gitaa na muziki mwingi zaidi. Wengi wanakubali kwamba Copilot inafanya kuwa rahisi kuchukua programu - kama Gift alipata. "Rust ina sifa ya kuwa lugha ngumu sana", anasema. "Lakini nilishangazwa sana na jinsi wanafunzi walivyofanya vizuri na kwa miradi waliyoijenga - jinsi ilivyokuwa ngumu na muhimu. "" Gift anasema waliweza kujenga programu kamili za wavuti na mazungumzo ndani yao. """ Hata hivyo, si kila mtu aliyekuwa na furaha na mabadiliko ya mtaala ya Gift. "Mmoja wa walimu wake aliwaambia wanafunzi wapya wasitumie ""Copilot"" kwa sababu ilikuwa ni kitu ambacho kingewazuia kujifunza vizuri." Zawadi inakubali kwamba Copilot ni kama magurudumu ya mafunzo ambayo huenda usitake kuondoa. Lakini haoni kwamba hiyo ni tatizo: "Tunajaribu kufanya nini? Sisi ni kujaribu kujenga mifumo tata. Na kufanya hivyo, yeye anadai, programmers lazima kutumia zana yoyote inapatikana. Ni kweli kwamba historia ya kompyuta imeona programu kutegemea tabaka zaidi na zaidi ya programu kati yao wenyewe na mashine code kwamba kompyuta inaweza kukimbia. Wameenda kutoka kadi za punch na msimbo wa mkusanyiko kwa lugha za programu kama Python ambazo ni rahisi kusoma na kuandika. Hiyo inawezekana kwa sababu lugha hizo hutafsiriwa katika nambari ya mashine na programu inayoitwa compilers. "Wakati nilianza programu katika miaka ya 80 na 90, bado ulihitaji kujua jinsi CPU ilivyofanya kazi", anasema Dohmke. <unk>Sasa wakati wa kuandika programu ya mtandao, wewe karibu kamwe kufikiri kuhusu CPU au seva ya mtandao.<unk> Kuongeza katika orodha ndefu ya bug-kuchukua na code-kupima zana, na watengenezaji wa programu ni kutumika kwa kiasi kikubwa cha msaada wa moja kwa moja. Kwa njia nyingi, Copilot na wengine ni wimbi la karibuni zaidi tu. "Nilitumia Python kwa miaka 25 kwa sababu iliandikwa ili iweze kusomwa na wanadamu,"" anasema Gift. <unk>Kwa maoni yangu, hiyo haina maana tena.<unk> Lakini yeye anaonyesha kwamba Copilot si bure kupita. "Mpiloti mwenza huonyesha uwezo wako", asema. <unk>Ni lifts kila mtu juu kidogo, lakini kama wewe <unk>ni programu duni wewe<unk>taendelea kuwa na udhaifu.<unk> Kazi ya kufanyika tatizo kubwa na kutathmini athari ya kweli ya zana kama ni kwamba wengi wa data bado ni anecdotal. "Dhahmke alisema: ""Utafiti wa GitHub ulionyesha kuwa waandaaji wa programu walikuwa wakikubali 30% ya mapendekezo (30% ni nje ya ulimwengu huu katika hali yoyote ya tasnia), lakini haijulikani ni kwa nini waandaaji wa programu walikubali mapendekezo hayo na kukataa mengine." Utafiti huo pia ulifunua kwamba programu wasio na uzoefu walikubali mapendekezo zaidi na kwamba programu walikubali mapendekezo zaidi walipokuwa wakizoea zana hiyo - lakini, tena, sio kwa nini. "Tunahitaji kwenda kina zaidi kuelewa nini maana yake", anasema Iansiti. <unk> Kuna kazi ya kufanywa kwa kweli kupata hisia ya jinsi mchakato coding yenyewe ni kuendeleza, na kwamba kazi ni wote TBD.<unk> Wengi masomo ya kujitegemea ya zana kama Copilot wamezingatia usahihi wa kanuni kwamba wao kupendekeza. Kama mifano yote kubwa ya lugha, zana hizi zinaweza kuzalisha nonsense. Pamoja na kanuni inaweza kuwa vigumu kusema - hasa kwa watumiaji wasio na uzoefu, ambao pia wanaonekana kutegemea Copilot zaidi. Timu kadhaa za watafiti katika miaka michache iliyopita wamegundua kwamba Copilot inaweza kuingiza mdudu au mapungufu ya usalama katika nambari. GitHub imekuwa busy kuboresha usahihi wa mapendekezo ya Copilot. "Kama ilivyoelezwa hapo juu, toleo la hivi karibuni la chombo hicho linaendesha nambari kupitia mfano wa pili uliofundishwa kuchuja makosa ya kawaida ya usalama kabla ya kutoa pendekezo kwa watumiaji. """ Lakini kuna masuala mengine ya ubora zaidi ya mdudu, anasema Dakhel. "Wakati huo huo, Copilot inaweza kupendekeza nambari ambayo ni ngumu sana au haifai kwa kile wataalamu wanachokiona kama mazoea bora, ambayo ni tatizo kwa sababu nambari ngumu au isiyo wazi ni ngumu kwa watu wengine kusoma, kuangalia, na kupanua.""" Tatizo ni kwamba mifano ni nzuri tu kama data yao ya mafunzo. Mifano ya Copilot ilitengenezwa kwa kutumia maktaba kubwa ya nambari zilizochukuliwa kutoka kwenye hifadhi ya mtandaoni ya GitHub, ambayo inarudi nyuma miaka 15. Nambari hii ina si tu bugs lakini pia mapungufu ya usalama ambayo hayakujulikana kuhusu wakati nambari iliandikwa. "Kujumuisha hii ukweli kwamba programu wasio na uzoefu kutumia chombo zaidi kuliko wale wenye uzoefu, na inaweza kufanya kazi zaidi kwa timu ya maendeleo ya programu kwa muda mrefu, ""Dakhel anasema." Huenda watengenezaji wa programu wenye ustadi wakalazimika kutumia wakati mwingi zaidi kukagua tena nambari iliyowekwa na wasio wataalamu. Dakhel sasa anatumaini kuchunguza pengo kati ya wataalamu na wasio wataalamu wa programu kwa undani zaidi. Kabla ya Copilot kutolewa, yeye na wenzake walikuwa wakitumia kujifunza mashine kugundua waandaaji wa programu wa wataalamu kwa nambari yao. Lakini Copilot alichanganya na data yake kwa sababu sasa ilikuwa vigumu kujua kama code ilikuwa imeandikwa na mtaalamu programu au moja chini ya uzoefu na msaada AI. Baada ya kufanya kazi na Copilot, ana mpango wa kutumia mbinu yake kujifunza ni aina gani ya kuongeza inatoa. "Nina hamu ya kujua kama watengenezaji wachanga kutumia chombo kama hicho itakuwa alitabiri kuwa watengenezaji mtaalam au kama bado ni detectable kwamba wao ni watengenezaji wachanga", anasema. <unk>Inaweza kuwa njia ya kupima jinsi kubwa ngazi juu ya zana hizi kutoa watu. " Hatimaye, huenda tusilazimike kungojea kwa muda mrefu kabla ya baraza la mahakama kufanyika. Maendeleo ya programu ni moja ya zaidi vizuri kumbukumbu na kikamilifu kipimo cha shughuli za biashara. Ikiwa Copilot inafanya kazi, itatumiwa. Ikiwa haifanyi hivyo, haifanyi hivyo. Wakati huohuo, zana hizi zinazidi kuwa bora. Hata hivyo ni muhimu kutambua kwamba programu <unk> kuandika maandishi juu ya skrini <unk> ni sehemu ndogo ya kazi ya jumla ya maendeleo ya programu. Inahusisha kusimamia sehemu nyingi za fumbo tata, ikiwa ni pamoja na kubuni kanuni, kuijaribu, na kuitumia. Copilot, kama programu nyingi kabla yake, inaweza kufanya sehemu ya kazi hiyo kwa kasi zaidi, lakini si reinvent yake kabisa. "Kila wakati kutakuwa na watengenezaji wa programu", anasema Synnaeve. <unk>Watapata msaada mwingi, lakini mwishowe kile kinachohusika ni kuelewa ni matatizo gani yanahitaji kutatuliwa. "Kufanya hivyo vizuri na kutafsiri kuwa programu ni kazi ya programu. ""AI kwa kila kitu: 10 teknolojia ya mafanikio 2024"" Vifaa vya Generative AI kama vile ChatGPT vilipata kupitishwa kwa wingi kwa muda wa rekodi, na kuweka upya mwendo wa sekta nzima." "Wanahabari wetu wanaangalia mwenendo nne moto wa kuangalia kwa mwaka huu: OpenAI inashangaza mfano mpya wa video ya kizazi kinachoitwa ""Sora"" Kampuni inashiriki Sora na kikundi kidogo cha majaribio ya usalama, lakini wengine wetu watalazimika kusubiri kujifunza zaidi." Google's Gemini sasa iko katika kila kitu. Hapa ni jinsi unaweza kujaribu nje. Gmail, Docs, na zaidi sasa itakuja na Gemini iliyotengenezwa. Lakini Wazungu watalazimika kusubiri kabla ya kupakua programu hiyo. Kupata updates kutoka MIT Technology Review, kugundua inatoa maalum, hadithi kuu, matukio ya hivi karibuni, na zaidi.