Battle of the blockchains; Ethereum and its rivals.

"The race to dominate the DeFi ecosystem is on

T O BELIEVERS, OPEN, public blockchains provide a second chance at building a digital economy. The fact that the applications built on top of such blockchains all work with each other, and that the information they store is visible to all, harks back to the idealism of the internet's early architects, before most users embraced the walled gardens offered by the tech giants. The idea that a new kind of "decentralised" digital economy might be possible has been bolstered over the past year as the numerous applications being built on top of various blockchains have boomed in size and functionality.

Perhaps the most significant part of that economy has been decentralised-finance (DeFi) applications, which enable users to trade assets, get loans and store deposits. Now an intensifying battle for market share is breaking out in this area. Crucially, Ethereum, the leading DeFi platform, seems to be losing its near-monopoly. The struggle shows how DeFi is subject to the standards wars that have broken out in other emerging technologies--think of Sony Betamax versus VHS video cassettes in the 1970s--and illustrates how DeFi technology is improving lightning-fast.

The idea behind DeFi is that blockchains--databases distributed over many computers and kept secure by cryptography--can help replace centralised intermediaries like banks and tech platforms. The value of assets stored in this nascent financial system has climbed from less than $1bn at the start of 2020 to more than $200bn today.

Until recently the Ethereum blockchain was the undisputed host of all this activity. It was created in 2015 as a more general-purpose version of Bitcoin. Bitcoin's database stores information about transactions in the associated cryptocurrency, providing proof of who owns what at any time. Ethereum stores more information, such as lines of computer code. An application that can be programmed in code can be guaranteed to operate as written, thereby removing the need for an intermediary. But just as Ethereum improved upon Bitcoin, it too is now being usurped by newer, better technology. The fight resembles competition between operating systems for computers, says Jeremy Allaire, the boss of Circle, a firm that issues USD Coin, a popular crypto-token.

Current blockchain technology is clunky. Both Bitcoin and Ethereum use a mechanism called "proof of work", where computers race to solve mathematical problems to verify transactions, in return for a reward. This slows the networks down and limits capacity. Bitcoin can process only seven transactions per second; Ethereum can handle only 15. At busy times transactions are either very slow or very costly (and sometimes both). When demand to complete transactions on Ethereum's network is high, the fees paid to the computers that verify them climb and settlement times grow. Your correspondent has paid as much as $70 to convert $500 into ether and waited for several minutes for a transfer from one crypto-wallet to another to take place.

Developers have long been trying to improve Ethereum's capacity. One prong of that is, in effect, rewiring it. Plans are afoot to shift Ethereum to a more easily scalable mechanism called "proof of stake" later this year. Another idea is to split the blockchain up, through a process called "sharding". The shards will share the load, expanding capacity. Some developers are also working on ways to bundle transactions, reducing the number of them that must be directly verified.

The problem is that each advance comes with costs. DeFi's supporters tout the virtue of being able to conduct transactions securely and without centralised intermediaries. But gains in scale could come at a price, by making the platform less secure, or less decentralised. Pooling transactions before they reach the blockchain tends to be done by centralised entities. And it might be easier for hackers to attack a single shard of a blockchain than the entire thing. As a result, Ethereum developers have been slow to make changes.

This sluggishness has made the network vulnerable in a different way--by encouraging rivals. In early 2021 nearly all of the assets locked in DeFi applications were on Ethereum's network. But in a recent research note JPMorgan Chase, a bank, estimates that the share of DeFi applications using Ethereum fell to 70% by the end of 2021. A growing number of networks, such as Avalanche, Binance Smart Chain, Terra and Solana, now use proof of stake to run blockchains that do the same basic job as Ethereum, but much more quickly and cheaply. Avalanche and Solana, for instance, both process thousands of transactions a second.

The experience of USD Coin illustrates these shifts. The token was launched on Ethereum just over three years ago, but has since been launched on a number of competitor networks, including Algorand, Hedera and Solana. Mr Allaire says that whereas transactions on Ethereum are subject to cost and speed limitations, those on Solana can handle "Visa-scale volumes" with "settlement finality in about 400 milliseconds and a transaction cost of about a twentieth of a penny". Other DeFi applications, like SushiSwap, an exchange founded on Ethereum, have also launched on several other blockchains.

With the planned changes to Ethereum likely to take at least a year, if not longer, "the risk is that the Ethereum network will lose further market share", wrote Nikolaos Panigirtzoglou of JPMorgan. For Mr Allaire, the picture is pleasingly competitive: "Just like with the web, where Windows, i OS and Android all compete, there are competing blockchain platforms, too." He thinks the ultimate victor will be the platform that attracts the best developers to build applications and therefore reaps network effects.

But the operating-system metaphor may only extend so far, in part because of the nature of open, public blockchains. Anyone can access the data they produce and view their operating code, making it possible to build bridges or applications that work across many blockchains, or which aggregate information from different blockchains. Some applications, like 1inch, already scan exchanges on several blockchains in order to find the best execution prices for various crypto transactions. "Multi-chain" blockchains, like Polkadot and Cosmos, act like bridges between different networks, making it possible to work across them.

For as long as decentralised finance holds promise, competition to be the network of choice will naturally be fierce. But the idea that the eventual winner will take everything, gaining overall control over the digital economy and how it develops, may one day come to seem as outdated as the video cassette." [1]

·  ·  ·  1. "Battle of the blockchains; Ethereum and its rivals." The Economist, 22 Jan. 2022, p. 67(US).

 

Kinija nori sukurti pasaulinę DI pramonę. Nesulaikykite kvėpavimą

  „Į PIETUS nuo Huai upės per lietų ir sniegą matosi kelios žąsys“. Klasikinėje kinų kalboje ši eilutė yra proveržis – ne literatūroje, o skaičiavimo galioje. Dirbtinio intelekto (DI) kalbos modelio Wu Dao 2.0 sudaryta eilutė metrais ir tonu nesiskiria nuo senovės poezijos. Laboratorija, sukūrusi programinę įrangą, Pekino dirbtinio intelekto akademija (BAAI), ragina savo svetainės lankytojus atskirti Wu Dao nuo kūno ir kraujo VIII amžiaus meistrų. Anekdotiniai įrodymai rodo, kad tai apgauna daugumą bandytojų.

 

    Sistema, kurios pavadinimas reiškia „nušvitimas“ ir kuri gali imituoti žemesnius kalbos tipus, savo galią gauna iš neuroninio tinklo su 1,75 trn kintamaisiais ir kitomis įvestimis. GPT-3, panašus modelis, kurį metais anksčiau sukūrė tyrėjų komanda San Franciske ir tuo metu buvo laikomas įspūdingu, įvertino tik 175 mlrd. kintamųjų. Tokiu būdu Wu Dao reiškia šuolį tokio tipo mašininiame mokyme, kuris bando pamėgdžioti žmogaus smegenų veiklą. 

 

Tai džiugina klasikinės literatūros gerbėjus, bet ne taip, kaip Pekino komunistų valdžia, kuri DI padarė į pagrindinį Kinijos technologinį ir ekonominį planą, pirmą kartą parengtą 2017 m. Tai gąsdina Vakarų vyriausybes, kurios nerimauja dėl DI daugiau kenksmingo pritaikymo tokiose srityse kaip stebėjimas ir karas. Ir tai intriguoja investuotojus, kurie ieško didžiulės verslo galimybės.

 

    Iš pirmo žvilgsnio plano pradžia yra gera. JD.com, elektroninės prekybos grupės, logistikos padalinys valdo vieną pažangiausių pasaulyje automatizuotų sandėlių netoli Šanchajaus. Gegužės mėnesį Kinijos paieškos milžinas Baidu Pekine paleido taksi be vairuotojo. „SenseTime“ „išmaniojo miesto“ AI modeliai – miesto stebėjimo kameros, kurios seka viską nuo eismo įvykių iki nelegaliai pastatytų automobilių – buvo dislokuoti daugiau, nei 100 miestų Kinijoje ir užsienyje. Kinija diegia daugiau pramoninių robotų su dirbtiniu intelektu, nei bet kuri kita šalis. O 2020 m. jis aplenkė Ameriką pagal citavimo žurnalus šioje srityje.

 

    Tačiau pažvelkite ne tik į antraštes ar elegantiškas Wu Dao eiles, ir viskas atrodo sudėtingiau. Taip, Kinija padarė pažangą dirbtinio intelekto srityje ir netgi retkarčiais pasirodydama dideliais taškais, tokiais kaip Wu Dao. Tačiau beveik neabejotinai ji vis dar atsilieka nuo Amerikos tiek investicijų, tiek pažangiausių inovacijų prasme. 2020 m., praėjus trejiems metams nuo pagrindinio plano, privačios Kinijos dirbtinio intelekto įmonės gavo mažiau, nei pusę investicijų, nei jų kolegos iš Amerikos. Ir daugelis viešųjų ir privačių pinigų, patenkančių į sektorių, gali būti iššvaistyti.

 

    Penkerių metų senumo Kinijos DI pagrindiniame plane nustatyta keletas tikslų. Pavyzdžiui, iki 2025 m. šalis turi sukurti pramonę, kurios pasaulinės pajamos sieks 400 mlrd. juanių, pasiekti „didelių proveržių“ technologijų srityje ir pirmauti pasaulyje pagal kai kurias programas. Po penkerių metų ji turi dominuoti pramonėje (tuo metu jos pardavimas bus 1 trilijonas dolerių), parašęs savo etikos kodeksą ir nustatęs techninius standartus, kaip Europa ir Amerika apibrėžė pramonės revoliucijos kontūrus.

 

    Komunistų partijos požiūrio elementai yra būdingi preskriptyvūs. Mokslo ir technologijų ministerija nurodė Kinijos technologijų milžinams, užsiimantiems tam tikromis dirbtinio intelekto subdisciplinomis – „Tencent“ – medicininio vaizdo atpažinimo srityje, „Baidu“ – autonominio vairavimo srityje, – padvigubinti jų veiklą. Be to, planas yra mažiau praktiškas, nei kai kurie kiti šalies plėtros projektai, pastebi Jay Huang iš Bernstein, investicinės įmonės. Pasak Huw Robertso iš Oksfordo universiteto ir penkių bendraautorių, projektas daugiausia veikia, kaip „patvirtinimo antspaudas“, kuris „pagiria“ įvairias dirbtinio intelekto iniciatyvas, kurias palaiko centrinės vyriausybės subjektai, vietos valdžios institucijos ir privatus sektorius.

 

    Praktiškai pagyrimas reiškia daug viešųjų pinigų. Dalis to yra mokesčių lengvatos ir subsidijos, kaip ir „mažųjų milžinų“ programoje, skirtoje įvairiuose sektoriuose, įskaitant dirbtinį intelektą, ugdyti 10 000 perspektyvių startuolių. Vietos valdžia, net ir skurdžiose rūdžių juostos provincijose, tokiose, kaip Liaoningas tolimuose šiaurės rytuose, taip pat teikė panašias paskatas dirbtinio intelekto srityje smalsaujančioms įmonėms.

 

    Kita paramos rūšis gaunama iš viešųjų pirkimų. Firmos neatskleidžia, kiek pajamų jos gauna iš viešojo sektoriaus sutarčių. Tačiau dalis, greičiausiai, bus reikšminga. Centrinės ir vietos valdžios institucijos naudoja „SenseTime“ stebėjimo technologiją. „Megvii“, kuri taip pat specializuojasi įvaizdžio atpažinimo srityje, turi daug ryšių su valstybinėmis įmonėmis.

 

    Valstybė taip pat tiesiogiai investuoja į dirbtinio intelekto įmones. Centrinė valdžia valdo keletą technologijų investicijų transporto priemonių. Vietos valdžia vis dažniau kuria savo, dažnai apsiginklavusi milijardais dolerių.

 

    Vyriausybės kapitalas vis labiau padeda užpildyti spragą, kurią paliko užsienio investuotojai, išgąsdinti Amerikos sankcijų kai kuriems Kinijos DI numylėtiniams, kurie laikomi pernelyg artimais komunistų partijai. Kinijos kibernetinės erdvės administracijos, reguliuotojo, valdomas fondas įsigijo neskelbiamą „SenseTime“ akcijų, kuriai praėjusį mėnesį buvo pritaikytas dar vienas Amerikos sankcijų raundas dėl tariamo dalyvavimo vyriausybės represijose prieš uigūrų etninę mažumą. („SenseTime“ teigia, kad sankcijos yra pagrįstos „klaidingu jos verslo suvokimu“.) Atskira transporto priemonė – Mišrios nuosavybės reformos fondas – sudarė 200 mln. dolerių iš 765 mln. dolerių, kuriuos įmonė surinko per pirminį viešąjį siūlymą (IPO). Vietos valdžia skyrė dar 220 mln. dolerių.

 

    Pamestas vertime

 

    Valdžios pinigai kartu su prieiga prie daugybės viešųjų duomenų padėjo Kinijos dirbtinio intelekto įmones paversti galingomis tam tikrose nišose.

 

    Anot konsultacinės įmonės Bain, iki praėjusių metų birželio mėn. Alibaba, Kinijos elektroninės komercijos didvyris, debesų padalinys siūlė 62 DI remiamasas paslaugas nuo balso atpažinimo iki vaizdo analizės, palyginti su 47 savo artimiausio konkurento Vakarų „Microsoft“ paslaugomis. 

 

„SenseTime“ ir „Megvii“ masiškai gamina kompiuterinio matymo programinę ir techninę įrangą, kurią galima pritaikyti ir įdiegti atskirose gamyklose. Nepaisant to, kad Amerikos sankcijomis buvo užblokuota daugelyje Vakarų rinkų, 2020 m. „SenseTime“ uždirbo 762 mln. juanių užsienyje, palyginti su 319 mln. juanių prieš dvejus metus, daugiausia iš Pietryčių Azijos.

 

    Nepaisant visų šių sėkmių, Kinijos AI pramonė svarbiais būdais atsilieka nuo Vakarų. Nepaisant to, kad Amerika pirmauja pagal bendrą su dirbtiniu intelektu susijusių leidinių skaičių, Kinija parengia mažiau recenzuojamų straipsnių, turinčių akademinių ir įmonių bendraautorių arba pristatomų konferencijose, kurios abi paprastai rengiamos pagal aukštesnius standartus. Pagal kvalifikuotų dirbtinio intelekto programuotojų skaičių, palyginti su jos gyventojų skaičiumi, ji yra žemiau Indijos ir gerokai žemiau Amerikos. Tikėtina, kad šie trūkumai išliks dėl trijų priežasčių.

 

    Pirma, kapitalas gali būti paskirstytas neefektyviai. Pavyzdžiui, neaišku, kiek Tiandzino 16 mlrd. dolerių vertės subsidijos iš tikrųjų buvo panaudota. Dar daugiau žalos padarydamas, Pekinas sukūrė vietos pareigūnų atlyginimo sistemą, kuri skatina išlaidauti su skolomis ir retai baudžia už švaistymą.

 

    Daugelis valstybės DI investicijų buvo „neapgalvotos ir perteklinės“, sako Jeffrey Dingas iš Stanfordo universiteto. Zeng Jinghan iš Lankasterio universiteto dokumentavo, kad daugėja įmonių, kurios klaidingai teigia, kad kuria dirbtinį intelektą, kad gautų subsidijas. Viena konsultacinės įmonės Deloitte analizė apskaičiavo, kad 2018 m. 99 % DI startuolių buvo netikri. Ponas Dingas pažymi, kad tokie sunkumai ne tik išeikvoja viešuosius pinigus, bet ir sunaudoja žmogiškąjį kapitalą, kurį būtų buvę naudingiau panaudoti kitur.

 

    Antroji Kinijos problema yra jos nesugebėjimas įdarbinti geriausių pasaulyje dirbtinio intelekto protų, ypač tų, kurie dirba su aukšto lygio moksliniais tyrimais. 2020 m. atliktas MacroPolo, Čikagoje įsikūrusios ekspertų grupės, tyrimas parodė, kad daugiau, nei pusė aukščiausio lygio mokslininkų dirbo ne savo šalyse. Amerika ir Europa atrodo patraukliau tokioms palaidoms smegenų dėžėms, įskaitant daugelį kiniškų. Nors maždaug trečdalis geriausių pasaulyje dirbtinio intelekto talentų yra iš Kinijos, iš tikrųjų ten dirba tik dešimtadalis. Ne Kinijos mokslininkų trūkumas dar labiau pablogina Kinijos galimybes, pažymi Mattas Sheehanas iš Carnegie Endowment for International Peace, ekspertų grupės Vašingtone.

 

    Dar problemiškesnis partijai, jos pagrindinis planas ignoravo pažangiausius puslaidininkius, kurie maitina AI. Nuo tada, kai jis buvo paskelbtas, Kinijos įmonėms tapo vis sunkiau įsisavinti pažangias kompiuterių lustus. Taip yra todėl, kad beveik visi tokie mikroprocesoriai yra amerikietiški arba pagaminti su amerikietiška įranga. Jiems taikomi eksporto į Kiniją apribojimai, kuriuos nustatė Donaldas Trumpas ir pratęsė jo įpėdinis prezidento poste Joe Bidenas. Prireiks ne vienerių metų, kol Kinijos įmonės pasivys pasaulines pažangiausias technologijas, jei jos iš viso galės tai padaryti.

 

    Šie iššūkiai ir toliau vargins visas Kinijos aukštųjų technologijų pramonės šakas ateinančiais metais. Ji gali įstrigti DI verslui – sėkmingai diegti palyginti nesudėtingus produktus, o Europa ir Amerika aplenktų paradigmas keičiančiose srityse, turinčiose didesnę finansinę ir strateginę vertę. Apsvarstykite Wu Dao 2.0. Nors buvo didžiulis GPT-3 patobulinimas, jis padarė būtent tai – patobulino esamą technologiją, o ne išlaužė naują kelią. Tikėtina, kad jokie Kinijos mokesčių mokėtojų pinigai to nepakeis.“ [1]

 

Kinija yra pasaulio fabrikas. Tai atveria plačias galimybes DI pritaikymui gamyboje, gaminiuose ir paslaugose. DI arba dirba (pvz., teisingai suskirsto vaizdus testuose), arba ne. Genijai mokslininkai, žiauriai sudėtingi puslaidininkiai gali pasirodyti nereikalingi DI pritaikyme mūsų kasdienybėje, tame tarpe ir karo srityje. Todėl snausti ant laurų, kartu su tais genijais mokslininkais tikrai nevertėtų.

 

·  ·  · 1. "In search of mastery; Artificial intelligence." The Economist, 22 Jan. 2022, p. 57(US).

China in search of mastery; Artificial intelligence.

China wants to create a world-beating AI industry. Don't hold your breath

"SOUTH OF THE Huai river few geese can be seen through the rain and snow." In classical Chinese this verse is a breakthrough--not in literature but in computing power. The line, composed by an artificial intelligence (AI) language model called Wu Dao 2.0, is indistinguishable in metre and tone from ancient poetry. The lab that built the software, the Beijing Academy of Artificial Intelligence (BAAI), challenges visitors to its website to distinguish between Wu Dao and flesh-and-blood 8th-century masters. Anecdotal evidence suggests that it fools most testers.

The system, whose name means "enlightenment" and which can emulate lowlier types of speech, derives its power from a neural network with 1.75trn variables and other inputs. GPT-3, a similar model built a year earlier by a team of researchers in San Francisco and deemed impressive at the time, considered just 175bn parameters. As such Wu Dao represents a leap in this type of machine learning, which tries to emulate the workings of the human brain. That delights fans of classical literature--but not as much as it does the Communist authorities in Beijing, which have put AI at the heart of China's technological and economic master plan first set out in 2017. It spooks Western governments, which worry about AI's less benign applications in areas like surveillance and warfighting. And it intrigues investors, who spy a huge business opportunity.

On the face of it, the plan is off to a good start. The logistics arm of JD.com, an e-commerce group, operates one of the world's most advanced automated warehouses near Shanghai. In May Baidu, China's search giant, launched driverless taxis in Beijing. SenseTime's "smart city" AI models--urban surveillance cameras that track everything from traffic accidents to illegally parked cars--have been deployed in more than 100 cities in China and overseas. China has been deploying more AI-assisted industrial robots than any other country. And in 2020 it surpassed America in terms of journal citations in the field.

The five most prominent listed Chinese AI specialists are collectively worth nearly $120bn. The biggest of them, Hikvision, has a market value of $60bn. SenseTime, which went public in Hong Kong on December 30th, is worth $28bn. Two more are expected to list soon. In 2020 investments in unlisted AI startups reached $10bn, according to the AI Index compiled by researchers at Stanford University. In its prospectus SenseTime forecasts that revenues from AI-assisted image-recognition and computer-vision software, the most mature part of the market, could hit 100bn yuan ($16bn) by 2025, up from 24bn yuan in 2021.

Look beyond the headlines or Wu Dao's elegant verses, however, and things look more complicated. Yes, China has made progress on AI, and even the occasional big splash like Wu Dao. But it almost certainly still lags behind America in terms of both investment and cutting-edge innovation. In 2020, three years into the master plan, privately held Chinese AI firms received less than half as much investment as their American counterparts. And a lot of the public and private money pouring into the sector may end up being wasted.

China's five-year-old AI master plan set out a number of goals. For example, by 2025 the country is to create an industry with global revenues of 400bn yuan, achieve "major breakthroughs" in technology and lead the world in some applications. Five years later it is to dominate the industry (by then worth $1trn in sales), having written its ethical code and set its technical standards, just as Europe and America defined the contours of the Industrial Revolution.

Elements of the Communist Party's approach are characteristically prescriptive. The Ministry of Science and Technology has instructed China's tech giants with existing ventures in certain subdisciplines of AI--Tencent in medical image recognition, Baidu in autonomous driving--to double down on these. That said, the plan is less hands-on than some of the country's other development projects, observes Jay Huang of Bernstein, an investment firm. In the words of Huw Roberts of Oxford University and five co-authors, the blueprint acts chiefly as a "seal of approval" which "derisks" assorted AI initiatives championed by central-government entities, local authorities and the private sector.

In practice, the derisking involves doling out lots of public money. Some of this takes the form of tax breaks and subsidies, as in the "little giants" programme to nurture 10,000 promising startups across various sectors, including AI. Local governments, even in poor rustbelt provinces such as Liaoning in the far north-east, have also dangled similar incentives in front of AI-curious companies.

Another type of support comes from government procurement. Firms do not disclose how much revenue they derive from public-sector contracts. But the share is likely to be significant. Central and local authorities use SenseTime's surveillance technology. Megvii, which also specialises in image recognition, has extensive dealings with state-owned enterprises.

The state is also investing in AI companies directly. The central government runs several tech-investment vehicles. Local governments are increasingly creating their own, often armed with billions of dollars. Tianjin, a coastal metropolis, announced a $16bn AI fund in 2018.

Government capital is increasingly helping plug a gap left by foreign investors scared away by American sanctions against some of China's AI darlings, which are seen as being too close to the Communist Party. A fund run by the Cyberspace Administration of China, a regulator, has acquired an undisclosed stake in SenseTime, which last month was hit by another round of American sanctions over its alleged involvement in government repression of the Uyghur ethnic minority. (SenseTime says that the sanctions are based on a "misperception" of its business.) A separate vehicle, the Mixed-Ownership Reform Fund, accounted for $200m of the $765m that the firm raised in its initial public offering (IPO). Local governments chipped in another $220m.

Lost in translation

State dosh, combined with access to plentiful public data, has helped turn Chinese AI firms into powerhouses in certain niches.

According to Bain, a consultancy, by last June the cloud division of Alibaba, China's e-commerce behemoth, was offering 62 AI-enabled services, from voice recognition to video analytics, compared with 47 from its closest Western rival, Microsoft. SenseTime and Megvii mass-produce computer-vision software and hardware that can be adapted to and installed in individual factories. Despite being locked out of most Western markets by the American sanctions, SenseTime raked in 762m yuan in overseas revenues in 2020, compared with 319m yuan two years earlier, mostly from South-East Asia.

For all these successes, though, China's AI industry trails the West in important ways. Despite leading America in the overall number of AI-related publications, China produces fewer peer-reviewed papers that have academic and corporate co-authors or are presented at conferences, both of which are typically held to a higher standard. It ranks below India, and well below America, in the number of skilled AI coders relative to its population. These shortcomings are likely to persist, for three reasons.

First, capital may not be being allocated efficiently. It is unclear, for example, how much of Tianjin's $16bn kitty has actually been deployed. More damaging, Beijing has created a system for rewarding local officials that favours debt-fuelled spending and seldom punishes wastefulness.

Many state AI investments have been "reckless and redundant", says Jeffrey Ding of Stanford University. Zeng Jinghan of Lancaster University has documented the rise of firms that falsely claim to be developing AI in order to suck up subsidies. One analysis by Deloitte, a consultancy, estimated that 99% of self-styled AI startups in 2018 were fake. Such boondoggles not only burn through public cash, Mr Ding notes, but also consume scarce human capital that could more usefully have been deployed elsewhere.

China's second problem is its inability to recruit the world's best AI minds, especially those working on high-level research. A study in 2020 by MacroPolo, a Chicago-based think-tank, showed that more than half of top-tier researchers in the field were working outside their home countries. America and Europe look more appealing to such footloose brainboxes, including many Chinese ones. Though about a third of the world's top AI talent is from China, only a tenth actually works there. A shortage of non-Chinese researchers further handicaps China's capabilities, notes Matt Sheehan of the Carnegie Endowment for International Peace, a think-tank in Washington.

Even more problematic for the party, its master plan ignored the cutting-edge semiconductors that power AI. Since its publication Chinese companies have found it ever more difficult to get their hands on advanced computer chips. That is because virtually all such microprocessors are either American or made with American equipment. As such, they are subject to restrictions on exports to China put in place by Donald Trump and extended by his successor as president, Joe Biden. It will take years for Chinese companies to catch up with the global cutting-edge, if they can do it at all.

These challenges will continue to bedevil all of China's high-tech industries for years to come. It could leave its AI businesses stuck in a rut--successfully rolling out relatively unsophisticated products while trailing Europe and America in paradigm-shifting developments of greater financial and strategic value. Consider Wu Dao 2.0. Although it was a huge improvement on GPT-3, it did just that--improve an existing technology rather than break new ground. No amount of Chinese taxpayers' money is likely to change that." [1]

China is the world's factory. This opens up a wide range of applications for DI in manufacturing, in products and in services. DI either works (e.g., sorts images correctly in tests) or not. Genius scientists, brutally complex semiconductors may prove unnecessary in the application of DI in our daily lives, including in the military. Therefore, a nap on the laurels, along with those genius scientists, would definitely not be worth it.

 

·  ·  · 1. "In search of mastery; Artificial intelligence." The Economist, 22 Jan. 2022, p. 57(US).