Yize kubonakala sengathi sisalelwe yizinyanga ezimbalwa nje ukuthi sifinyelele ku-2020, lezi zinyanga zibalulekile nasemkhakheni wokuthuthukiswa kwesoftware. Lapha kulesi sihloko, sizobona ukuthi unyaka ozayo ka-2020 uzoshintsha kanjani izimpilo zabathuthukisi be-software!
Ukuthuthukiswa Kwesoftware Kwakusasa Kulapha!
Ukuthuthukiswa kwesofthiwe evamile kumayelana nokuthuthukisa isofthiwe ngokubhala ikhodi nokulandela imithetho ethile engaguquki. Kepha ukuthuthukiswa kwesoftware yanamuhla kubone ukuguquguquka kwepharadigm nokuthuthuka kwe-Artificial Intelligence, Ukufunda Ngomshini, kanye nokufunda Okujulile. Ngokuhlanganiswa kwalobu buchwepheshe obuthathu, abathuthukisi bazokwazi ukwakha izixazululo zesofthiwe ezifunda imiyalelo futhi bengeze izici ezengeziwe namaphethini kudatha edingekayo ukuze uthole umphumela oyifunayo.
Masizame Ngekhodi ethile
Ngokuhamba kwesikhathi, izinhlelo zokuthuthukisa isofthiwe yenethiwekhi ye-neural ziye zaba yinkimbinkimbi ngokwemibandela yokuhlanganisa kanye nezendlalelo zokusebenza nezixhumi ezibonakalayo. Onjiniyela bangakha inethiwekhi ye-neural elula kakhulu ngePython 3.6. Nasi isibonelo sohlelo olwenza ukuhlukaniswa okubili ngo-1 noma 0.
Kunjalo, singaqala ngokwakha isigaba senethiwekhi ye-neural:
ngenisa i-numpy njenge-np
X=np.array([[0,1,1,0],[0,1,1,1],[1,0,0,1]])
y=np.array([[0],[1],[1]])
Ukusebenzisa umsebenzi we-Sigmoid:
def sigmoid ():
return 1/(1 + np.exp(-x))
def derivatives_sigmoid ():
return x * (1-x)Ukuqeqesha Imodeli Ngesisindo Sokuqala Nokuchema:
epoch=10000
lr=0.1
inputlayer_neurons = X.shape[1]
hiddenlayer_neurons = 3
output_neurons = 1
wh=np.random.uniform(size=(inputlayer_neurons,hiddenlayer_neurons))
bh=np.random.uniform(size=(1,hiddenlayer_neurons))
wout=np.random.uniform(size=(hiddenlayer_neurons,output_neurons))
bout=np.random.uniform(size=(1,output_neurons))Kwabaqalayo, uma udinga usizo mayelana namanethiwekhi e-neural, ungathintana nawo .Noma, ungaqasha onjiniyela be-AI/ML ukuthi basebenze kuphrojekthi yakho.
Ikhodi Yokulungisa NgeNeuron Yesendlalelo Sokukhiphayo
hidden_layer_input1=np.dot(X,wh)
hidden_layer_input=hidden_layer_input1 + bh
hiddenlayer_activations = sigmoid(hidden_layer_input)
output_layer_input1=np.dot(hiddenlayer_activations,wout)
output_layer_input= output_layer_input1+ bout
output = sigmoid(output_layer_input)
Iphutha Lekubala Lesendlalelo Esifihliwe Samakhodi
E = y-output
slope_output_layer = derivatives_sigmoid(output)
slope_hidden_layer = derivatives_sigmoid(hiddenlayer_activations)
d_output = E * slope_output_layer
Error_at_hidden_layer = d_output.dot(wout.T)
d_hiddenlayer = Error_at_hidden_layer * slope_hidden_layer
wout += hiddenlayer_activations.T.dot(d_output) *lr
bout += np.sum(d_output, axis=0,keepdims=True) *lr
wh += X.T.dot(d_hiddenlayer) *lr
bh += np.sum(d_hiddenlayer, axis=0,keepdims=True) *lr
okukhipha:
print (output)
[[0.03391414]
[0.97065091]
[0.9895072 ]]Yize kuhlale kuwukuhlakanipha ukuhlala wazi ngezilimi zakamuva zokuhlela nezindlela zokubhala amakhodi, abahleli bohlelo kufanele bazi mayelana namathuluzi amaningi amasha asiza ukwenza izinhlelo zabo zokusebenza zihambisane nabasebenzisi abasha.
Ngo-2020, abathuthukisi be-software kufanele bacabangele ukuhlanganisa lawa mathuluzi angu-5 okuthuthukisa isofthiwe emikhiqizweni yabo ngokunganaki ukuthi basebenzisa luphi ulimi lohlelo:
1. Ukucubungula Ulimi Lwemvelo (NLP)
Nge-chatbot enika amandla isevisi yamakhasimende, i-NLP ithola ukunakwa kwabahleli bohlelo abasebenza ekuthuthukisweni kwesoftware yesimanje. Bafaka isicelo njengePython's ukuhlanganisa ngokushesha i-NLP kuma-chatbots, abasizi bedijithali, nemikhiqizo yedijithali. Maphakathi no-2020 noma maduze nje, uzobona i-NLP iba ββebaluleke kakhulu kukho konke kusuka ebhizinisini lokudayisa kuya ezimotweni ezizimele, namadivayisi ekhaya nasehhovisi.
Ukuqhubekela phambili ngamathuluzi angcono kakhulu wokuthuthukisa isoftware nobuchwepheshe, ungalindela ukuthi abathuthukisi bezinhlelo zesoftware basebenzise i-NLP ngezindlela eziningi ukusuka kusixhumi esibonakalayo somsebenzisi esiqhutshwa ngezwi kuye kube lula kakhulu ukuzulazula kumamenyu, ukuhlaziya imizwa, ukuhlonza umongo, imizwa, nokufinyeleleka kwedatha. Konke kuzotholakala kubasebenzisi abaningi futhi amabhizinisi angafinyelela ku-$430 wezigidigidi ezinzuzweni zokukhiqiza ngo-2020, ngokusho kwedatha ye-IDC ecashunwe yi-Deloitte.
2. I-GraphQL Ifaka I-REST Apis
Ngokusho konjiniyela befemu yami okuyinkampani yokuthuthukisa isofthiwe ye-offshore, i-REST API ilahlekelwa ukubusa kwayo endaweni yonke yohlelo lokusebenza ngenxa yokungasheshi ukulayisha idatha okudingeka yenziwe kuma-URL amaningi ngawodwana.
I-GraphQL iyithrendi entsha kanye nenye indlela engcono kakhulu yokwakhiwa kwe-Rest-based edonsa yonke idatha efanele kumasayithi amaningi ngesicelo esisodwa. Ithuthukisa ukusebenzisana kweseva yeklayenti futhi inciphise ukubambezeleka okwenza uhlelo lokusebenza luphendule kakhulu kumsebenzisi.
Ungathuthukisa amakhono akho okuthuthukisa isofthiwe uma usebenzisa i-GraphQL ekuthuthukiseni isofthiwe. Idinga futhi ukufaka amakhodi okuncane kune-REST Api futhi ivumela ukunika amandla imibuzo eyinkimbinkimbi phakathi kwemigqa embalwa elula. Ingabuye ihlinzekwe ngenani le iminikelo eyenza kube lula kubathuthukisi be-software ukuthi bayisebenzise ezilimini ezihlukene zokuhlela ezihlanganisa i-Python, i-Node.js, i-C++, ne-Java.
Okwamanje, i-GraphQL isekela umphakathi wabathuthukisi ngokwenza lokhu:
- Ukungavumeli izinkinga zokulanda nangaphansi
- Ukuqinisekisa nokuhlola uhlobo lwamakhodi
- Ukukhiqiza ngokuzenzakalelayo imibhalo ye-API
- Ngokunikeza imilayezo yephutha enemininingwane
- Engeza ukusebenza okwengeziwe etafuleni: "okubhaliselwe" ukuze uthole imilayezo yesikhathi sangempela evela kuseva
3.Phansi/Ayikho Ikhodi
Wonke amathuluzi okuthuthukisa isofthiwe yekhodi ephansi ahlinzeka ngezinzuzo eziningi. Kufanele isebenze kahle ngangokunokwenzeka ekubhaleni izinhlelo eziningi kusukela ekuqaleni. Ikhodi ephansi noma engekho inikeza ikhodi elungiselelwe ngaphambili engashumeka ezinhlelweni ezinkulu. Lokhu kuvumela ngisho nabangewona abenzi bezinhlelo ukuthi bakhe imikhiqizo eyinkimbinkimbi ngokushesha futhi kalula futhi basheshise i-ecosystem yesimanje yokuthuthukiswa.
Ngokombiko owabiwe ngu , amathuluzi wekhodi engekho/ephansi asevele asetshenziswa kumaphothali ewebhu, izinhlelo zesofthiwe, izinhlelo zokusebenza zeselula nezinye izindawo. Imakethe yamathuluzi ekhodi ephansi izokhula ifike ku-$15 bhiliyoni ngo-2020. Lawa mathuluzi aphatha yonke into efana nokuphatha ingqondo yokuhamba komsebenzi, isihlungi sedatha, ukungenisa, nokuthekelisa. Nawa amapulatifomu amakhodi aphansi/angenawo angcono kakhulu ongawalandela ngo-2020:
- I-Microsoft PowerApps
- I-Mendix
- Amasistimu angaphandle
- UMdali we-Zoho
- Salesforce App Cloud
- Isizinda Esisheshayo
- Ibhuthi yasentwasahlobo
4. Igagasi le-5G
Ukuxhumana kwe-5G kuzoba nomthelela omkhulu ekuthuthukisweni kweselula/kwesofthiwe, nokuthuthukiswa kwewebhu. Phela, kubuchwepheshe obufana ne-IoT yonke into ixhunyiwe. Ngakho-ke, isofthiwe yedivayisi izosebenzisa amafa angenantambo anesivinini esiphezulu ukuze abe namandla aphelele nge-5G.
Enkulumweni yamuva nje , uDan Dery, iphini likamongameli womkhiqizo kwaMotorola, uthe βEminyakeni ezayo, i-5G izoletha ukwabelana ngedatha okusheshayo, umkhawulokudonsa ophezulu, futhi isheshise isofthiwe yocingo ngokushesha izikhathi ezingu-10 kunobuchwepheshe obukhona obungenawaya.β
Ngalokhu, izinkampani zokuthuthukisa isofthiwe zizosebenzela ukufaka i-5G ezinhlelweni zesimanje. Ukukhishwa kwe-5G kuhamba ngokushesha, opharetha abangaphezu kuka-20 bamemezele ukuthuthukiswa kwamanethiwekhi abo. Ngakho-ke, abathuthukisi manje bazoqala ukusebenza ekuthatheni okufanele ukusizakala nge-5G. Ubuchwepheshe buzothuthukisa kakhulu lokhu okulandelayo:
- Ukuphepha kohlelo lwenethiwekhi, ikakhulukazi ekusikeni inethiwekhi.
- Izohlinzeka ngezindlela ezintsha zokuphatha ubunikazi bomsebenzisi.
- Izovumela ukwengeza ukusebenza okusha ezinhlelweni zokusebenza ezinezinga eliphansi lokubambezeleka.
- Izoba nomthelela ekuthuthukisweni kwesistimu enikwe amandla i-AR/VR.
5. "Ukuqinisekisa" okungenamsebenzi
Ukuqinisekisa kuya ngokuya kuba inqubo esebenzayo ekuvikeleni idatha ebucayi. Ubuchwepheshe obuyinkimbinkimbi abugcini nje ngokuba sengozini yokugebenga isofthiwe, kodwa futhi busekela ubuhlakani bokwenziwa kanye ne-quantum computing. Kepha imakethe yokuthuthukisa isoftware isivele ibona inqwaba yezinhlobo ezintsha zokuqinisekisa, njengokuhlaziywa kwezwi, i-biometrics, nokubonwa kobuso.
Kuleli qophelo, izigebengu ze-inthanethi bathola izindlela ezihlukene zokuguqula ubunikazi babasebenzisi be-inthanethi namaphasiwedi. Njengoba abasebenzisi beselula sebejwayele ukufinyelela kuma-smartphones abo ngesithupha noma ngomunwe noma ngokuskena ubuso, ngakho-ke ngamathuluzi okuqinisekisa ngeke badinge amakhono amasha okuqinisekiswa, kanye namathuba okuntshontshwa kwe-cyber azoba mancane. Nawa amanye amathuluzi okufakazela ubuqiniso ngezinto eziningi anombhalo wemfihlo we-SSL.
- Ama-Soft Tokens aguqula ama-smartphones akho abe iziqinisekisi zezinto ezisebenziseka kalula.
- Amaphethini e-EGrid iwuhlobo lokufakazela ubuqiniso okulula ukulisebenzisa noludumile embonini.
- Ezinye zesoftware yokuqinisekisa engcono kakhulu yamabhizinisi yilezi: I-RSA SecurID Access, i-OAuth, i-Ping Identity, i-Authx, ne-Aerobase.
Kunezinkampani zokuthuthukisa ama-software e-India nase-USA ezenza ucwaningo olunzulu kwisayensi yokuqinisekisa kanye ne-biometrics ngokuthuthukela ku-AI ukuletha isofthiwe yokuqinisekisa yezwi, ubuso, ukuziphatha, kanye ne-biometric. Manje, ungakwazi ukuvikela iziteshi zedijithali futhi uthuthukise amakhono ezinkundla.
Ama-Endnotes
Kubonakala sengathi impilo yabahleli bohlelo ngo-2020 izoba nzima kakhulu njengoba ijubane lokuthuthukiswa kwesoftware kungenzeka lisheshe. Amathuluzi atholakalayo azoba lula ukuwasebenzisa. Ekugcineni, le ntuthuko izoholela ekudaleni umhlaba onempilo ophokophele enkathini entsha yedijithali.
Source: www.habr.com