2022 is about to pass, and it is a special time of year to look forward to future development. As usual, Ai Times Frontier organizes a collection of articles about future technologies and trends for the benefit of readers.
Tomorrow is about to enter the New Year, and the 2023 Outlook series has also come to an end. Let us look forward to the new year together and more surprises in the field of artificial intelligence . I wish you all a happy new year and all the best.
In recent years, artificial intelligence has been growing at a rapid pace, and it seems difficult for anything to stop it. As momentum builds, which direction will AI take in 2023? Experts have something to say.
Many artificial intelligence projects are poorly conceived and ultimately fail. Zohar Bronfman, co-founder and CEO of Pecan AI, said that in 2023, enterprises will be more vigilant when evaluating the efficacy of artificial intelligence.
"In 2023, business leaders will evaluate potential data science projects more rigorously than in the past. Too often these projects fail to have real impact because they are not aligned with business needs, or because they never make it to production. With the expense and time commitment involved in data science, leaders will scrutinize proposed efforts more carefully and pursue the right plan." "We will continue to work to ensure that the output from the model drives business improvement actions in the short term or stops before resources are wasted," Bronfman said.
The demand for data scientists will continue to increase in 2023. Nick Elprin, CEO and co-founder of Domino Data Labs, predicts that the same will be true for GPU for deep learning model training.
“The biggest source of improvements in artificial intelligence has been the deployment of deep learning in training systems, especially Transformer models, tasks designed to simulate the actions of neurons in the human brain. These breakthroughs require enormous computing power to analyze large amounts of structured and unstructured data. According to the data set. Unlike CPU, graphics processing units (GPUs) can support the parallel processing required for deep learning workloads. This means that in 2023, as more applications based on deep learning technology emerge, from translating menus to treating diseases, the demand for GPUs will continue to soar."
Supporting this view is Charlie Boyle, vice president of DGX Systems at Nvidia, who hopes to sell more GPUs next year.
"In 2023, inefficient, x86-based legacy computing architectures that cannot support parallel processing will be replaced by accelerated computing solutions that will provide the computing performance, scale and efficiency needed to build language models, recommendation engines and more. Amid economic headwinds, enterprises will seek AI solutions that can achieve their goals while simplifying IT collaboration processes and improving efficiency . New platforms that use software to integrate workflows within infrastructure will enable breakthroughs in computing performance, lower total cost of ownership, reduce carbon footprints, accelerate return on investment for transformative AI projects, and replace more wasteful, older architectures."
How long do you think it will take to hire a qualified data scientist? Some people joke that it's as difficult as spotting a unicorn. Kyndi founder and CEO Ryan Welsh believes that 2023 will be the year when the world reaches "peak data scientist".
“The shortage of data scientists and machine learning engineers has been a bottleneck for enterprises in realizing the value of AI. Two things have happened as a result: more people are pursuing data science degrees and certifications, increasing the number of data scientists; and vendors have come up with new ways to minimize the involvement of data scientists in AI production. These two trends At the same time, disruption leads to "data scientist peak". Because as foundational models become available, companies can build their own applications on top of these models. Instead of requiring each company to train its own model from scratch, fewer data scientists are needed to train and more people are graduating. In 2023, the market is expected to respond accordingly, leading to data science oversaturation.
Expect to see ethical AI continue to attract corporate attention and resources, predicts Triveni Gandhi, head of AI at DataikuhtL2, a provider of data science tools.
“While we’ve seen some companies cutting ethical AI positions in the news, the reality is that most companies will continue to invest in their ethical AI teams. This resource is critical to scaling and operating AI, helping companies feel confident that their AI output is aligned with their values and executed in a robust and reliable manner. Additionally, the Ethical AI team Give users confidence that the products they are interacting with are considered and meet expectations for security and trust. Building an ethical AI team is a must for any company to stay ahead of the curve.”
One of the dilemmas with deep learning is the black-box nature of predictive models . Jans Aasman, CEO of graph database maker Franz, said one way to solve this problem is to pair artificial intelligence with causal knowledge graphs in 2023.
"The next few years will see growth in causal AI, starting with the creation of knowledge graphs that discover causal relationships between events. Healthcare, pharmaceuticals, financial services, , manufacturing, and supply chain organizations will connect domain-specific knowledge graphs to causal graphs and run simulations to move beyond correlation-based machine learning that relies on historical data. Causal prediction has the potential to increase the explainability of AI by making causal relationships transparent."
Maya Natarajan, senior director of product marketing at graph database maker Neo4j, also foresees significant progress in combining graphics and AI.
Natarajan said: "Enterprises will continue to look for the best ways to leverage knowledge graphs to enable responsible AI. By leveraging the context provided by knowledge graphs, organizations can improve the accuracy of ethical decision-making, improve explainability by keeping the data stream sourced, and help mitigate bias by opening up new analytical methods."
Artificial intelligence will find vector databases more attractive next year. That’s what Edo Liberty, founder and CEO of Pinecone, one of the early leaders in the vector database market, thinks.
"As AI continues to evolve and become more widely used, there will be a corresponding need for more advanced and scalable infrastructure to support its development and deployment. A key area of AI infrastructure investment will be specialized data infrastructure, such as vector databases, which are designed to store and process the large amounts of data generated by modern ML models. "This will accelerate the development and deployment of AI systems that will exceed the previous year's application in many areas," said Liberty. "
In recent years, companies have been increasing their use of artificial intelligence, with mixed results. But Kimberly, Business Solutions Manager, SAS Consulting Nevala predicts that in 2023, AI will enter a “less is more” growth phase.
“AI will proliferate as organizations realize “less is more” and quietly shift the focus from targeting large-scale innovation to applying it to a wider range of small decision points and actions whose collective impact is greater than the sum of its parts. Paradoxically, organizations and key employees need to have a broad understanding of these technologies and be comfortable using them. "
So you've invested heavily in the GPU to train your neural network . What do you do with it? There are always some SQL queries that require extra horsepower," said Matan Libis, vice president of product at SQream.
"The ability to reuse compute resources for AI/ML is an exciting and valuable opportunity for enterprises." Not only does reuse reduce the carbon footprint left by AI, but the general increase in cheaper global data storage solutions also reduces reliance on GPU hardware. Additionally, latency can be reduced when you don’t need to move data from one place to another.However, once enterprises prepare data in one place, train in another, and move inference to yet another, hopefully by streamlining the process, we will see huge improvements in the accuracy and speed of AI/ML capabilities.
Yonatan Geifman, CEO and co-founder of deep learning company Deci, said The high cost of cloud computing is putting a strain on everyone, but artificial intelligence users can combat rising costs by optimizing models.
"Enterprises that have been running artificial intelligence models in cloud environments are seeing the financial losses that high-performance cloud processing can cause them." 2023 may see more companies looking to reduce AI inference cloud costs. One of the most effective ways to achieve this is to increase the speed of AI models while maintaining their accuracy, reducing processing time on the cloud and effectively saving money. "
Evinced chief scientist Yossi Synett predicts that in 2023 we will see more breakthroughs in self-supervised machine learning technology that does not require labeled data.
"One factor hindering the development of artificial intelligence is the lack of high-quality labeled data. While we are already seeing progress today, growth will continue in 2023. We are looking at more and more ways to use self-supervised learning to pre-train models and then fine-tune the models for specific tasks. The best and most effective example of this is NLP (Natural Language Processing), where techniques called masked language modeling (making the model predict the hidden word in a sentence) and causal language modeling (making the model predict the next word in a sentence) have revolutionized the game. Since self-supervised learning requires no labeled data, fine-tuning requires much less, making it easier to train complex models. Can be used to better select labeled examples, which further reduces financial barriers to AI projects.
Chintan Mehta, CEO and Group CIO of Wells Fargo (Wells Fargo), said to be prepared for artificial intelligence to reach a higher level in 2023, adopting new user interaction models and better understanding of intent.
"In 2023 and beyond, the deployment of artificial intelligence and signal perception will accelerate exponentially." Artificial intelligence will defeat biased perception, judgment and legal interpretation . The industry will build more solutions to break bias so that AI can provide solutions to consumers while explaining its course of action. The user interface will transform. They will move beyond app-based experiences from non-visual tap/touch interactions to action calls that deliver vision to context and language and gesture-based interactions. The artificial intelligence required to power these experiences will increase dramatically, moving beyond just understanding language to truly grasping the hidden intent of every interaction. Artificial intelligence will produce artificial intelligence.
Marco Santos, U.S. CEO of German IT company GFT, predicts that in 2023, we will see unprecedented use cases for artificial intelligence and machine learning emerge and eventually become mainstream.
“We will see unprecedented use cases for artificial intelligence and machine learning as companies break away from the constraints of traditional systems and are able to bring together massive data sets from disparate systems.” For example, in the automotive manufacturing industry, we are just starting to see the emergence of next-generation manufacturing data platforms, or single and unified cloud-based platforms where manufacturers are aggregating all data across their entire organization. Once they have the data, they can start building AI applications. "
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-