Trends in the development of artificial intelligence technologies: the economic and legal aspect fo. 2)
Notice bibliographique
Résumé
The economic-legal analysis of the state and trends of the development of technologies of artificial intelligence (AI) has been carried out. The influence of AI on the development of society, economic effect, methods and the field of application, the state of developments in the world and Ukraine are analyzed. In the next decade, AI will become the main market trend and the best business tool. The contribution of intellectual technologies to global GDP is estimated at 15.7 trillion. dollars In the next 5-10 years, China will be the leader in the successful operation and adaptation of AI technologies. According to analysts, the most benefit from AI technologies will be in the areas of financial services, retail and medicine.The scientific and inventive activity in the sphere of AI, the role of protection of intellectual property (patent and copyright), and the maintenance of the balance of competing interests are researched. Recently, the number of inventions based on AI has sharply increased. The leaders in the number of such inventions are American companies IBM and Microsoft. This growth is due to the fact that in recent years AI has evolved from the theoretical concept into a real product that gains the world market. Since the advent of AI in the 50’s of the last century, inventors and researchers have applied for almost 340 thousand inventions based on AI (as of the end of 2016) and published more than 1.6 million scientific articles. The transport sector, including autonomous vehicles, is one of the sectors with the highest rates of growth in the application of AI. China has become a global leader in increasing the number of patents in the AI sphere over the past five years.By the number of companies working in the sphere of AI, Ukraine is among the three leaders among the countries of Eastern Europe. There are 57 AI companies in Ukraine and it has 11 investorsGeneralized practice of state regulation of activity in the sphere of AI in industrialized countries and EU countries. More and more countries are developing national AI strategies. Thus, 17 countries, including Canada, China, Denmark, France, India, South Korea and Taiwan, have already announced their AI strategies. Some of them invest billions of dollars in this area. China, for example, has invested more than $ 10 billion in this technological trend, followed by South Korea — $ 2 billion and France — $ 1.5 billion. Governmental structures from different countries are concerned about the need to develop relevant national strategies, programs and regulation of AI legislative level. Identified existing problems and suggested ways to solve them. Problems constraining the development of AI in Ukraine: the absence of a strategy for the development of AI, the domestic infrastructure for its work and the weakness of the business about existing fundamental scientific developments in the field of AI, insufficient for the implementation of AI level of digitalization of companies, the lack of a high level of data work, and is also a misunderstanding of the implementation guidance in the AI company.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,003 | 0,007 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,006 | 0,007 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,011 | 0,004 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».