AI democratization aims to create a world in which the average person can utilize AI techniques. To achieve this goal, numerous research institutes have attempted to make their results accessible to the public. In particular, large pre-trained models trained on large-scale data have shown unprecedented potential, and their release has had a significant impact. However, most of the released models specialize in the English language, and thus, AI democratization in non-English-speaking communities is lagging significantly. To reduce this gap in AI access, we released Generative Pre-trained Transformer (GPT), Contrastive Language and Image Pre-training (CLIP), Stable Diffusion, and Hidden-unit Bidirectional Encoder Representations from Transformers (HuBERT) pre-trained in Japanese. By providing these models, users can freely interface with AI that aligns with Japanese cultural values and ensures the identity of Japanese culture, thus enhancing the democratization of AI. Additionally, experiments showed that pre-trained models specialized for Japanese can efficiently achieve high performance in Japanese tasks.

While only about 1% of companies are making the most of their data today, real progress is being made in democratizing the use of AI, and the future of business automation via AI is quite bright, H2O.ai's CEO and founder Sri Ambati said before a pair of H2O World conferences this week. "There's still a long way to go from where we are. It's in the earliest phases of adoption," Ambati told Datanami in an interview earlier this month. "You can see that only 1%, or less than 1%, of the world's companies can truly leverage their data. So that means 99% needs further adoption, simplification, and cultural transformation to use data and AI. It's going to take the next 10 to 20 years."

Naveen is the Executive Director of Data and AI Services and a senior leader in the Applications, Data, and AI practice at Kyndryl. He's been an engineering leader, mentor, coach. His work has contributed actively in driving breakthrough innovations in the Data and AI solutions for Kyndryl. He's helped foster industry-academic partnerships and also advises start-ups using his background and experience.

We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - August 3. Join AI and data leaders for insightful talks and exciting networking opportunities. Along with all the analytical and operational gains artificial intelligence (AI) brings to the enterprise, there is another, more fundamental change taking place. As the technology becomes more adept at understanding human speech and intentions, we stand at the cusp of a dramatic transformation in the relationship between humans and the digital universe. Using techniques like natural language processing (NLP) and neural networking, AI will very likely bring an end to the graphical user and even command line interfaces, which require a fair amount of mastery to operate, in favor of a more conversational approach in which operators merely state what they want and the system understands and responds. That's right, no more clicking or tapping through endless menus, no more finding the right app – just ask, and it is yours.

AI helped in data gathering, data processing, data analytics, and all important automated protein molecule binding prediction. Many countries have rolled out Covid-19 vaccines, and several conducting dry runs to check preparedness. The WHO has extended emergency use approval. It has paved the way developing countries that don't have any infrastructure for trails. The world was speedily to realize the importance to share genome sequencing data, which accelerated the pace of vaccine development. It would have been possible the presence of AI and cloud computing.

A recent New York Times article fretting about the soaring costs of developing and training leading-edge deep learning models and my admittedly provocative Tweet questioning the premise and motives of the article's sources led to the type of online banter that indicates a nuanced question ill-suited for pithy Twitter responses. Fears of AI creating a chasm between haves and have-nots are common, however the topic of AI-fueled inequality typically centers on its economic effects, namely that the growing substitution of manual labor with algorithmic automation serves to further polarize income distributions as the knowledge class controlling and using the algorithms get richer while the working class being displaced by machines suffers. Many new technologies -- those we call'automation technologies' -- do not increase laborís productivity, but are explicitly aimed at replacing it by substituting cheaper capital (machines) in a range of tasks performed by humans. As a result, automation technologies always reduce the laborís share in value added (because they increase productivity by more than wages and employment). They may also reduce overall labor demand because they displace workers from the tasks they were previously performing.

Artificial intelligence (AI) is poised take a prominent place within organizations in 2018, and the coming year will mark the beginning of the democratization of AI. In other words, a much broader range of companies and government departments will use AI. "Throughout 2018, AI performance will continue to improve," says Chirag Dekate, research director at Gartner. "The increased availability of AI capabilities embedded in applications and platforms like cloud office suites and deep neural network (DNN)-based virtual assistants, such as Alexa and Siri, will boost intelligent conversational interfaces to products and services." Gartner's latest CIO survey of 3,160 CIOs from 98 countries, found that 21% of CIOs are already piloting AI initiatives or have short-term plans for them.