The landscape of artificial intelligence has witnessed numerous cycles of excitement, yet the introduction of ChatGPT appears to signal a pivotal moment, even for skeptics. OpenAI's chatbot, fueled by its cutting-edge large language model, showcases the ability to craft poems, jokes, and essays that convincingly emulate human creation. A mere prompt to ChatGPT can result in love poems masquerading as Yelp reviews or song lyrics echoing the style of Nick Cave.
In the past, generative AI took center stage with breakthroughs in computer vision, transforming selfies into Renaissance-style portraits and aging faces prematurely on social media. However, the current surge in natural language processing, specifically the prowess of large language models to creatively engage with diverse themes, has captivated popular imagination. Beyond language, generative models demonstrate proficiency in learning the grammar of software code, molecules, natural images, and various other data types.
The applications of this technology are expanding daily, ushering us into an era of exploration for its potential. At IBM Research, efforts are underway to empower customers in using generative models for accelerated software code development, the discovery of new molecules, and the training of reliable conversational chatbots grounded in enterprise data. Additionally, generative AI is being leveraged to generate synthetic data, addressing the need for robust and trustworthy AI models while respecting privacy and copyright laws.
As the field progresses, it becomes essential to revisit the fundamentals of generative AI, its evolution, and operational principles.
Understanding Generative AI Models
Generative AI refers to deep-learning models capable of taking raw data, such as the entirety of Wikipedia or the works of Rembrandt, and learning to generate statistically probable outputs in response to prompts. These models encode a simplified representation of their training data and draw from it to produce new content that shares similarities with, but is not identical to, the original data.
The ascent of deep generative models traces back to variational autoencoders (VAEs), introduced in 2013. VAEs, by making models easier to scale, played a pivotal role in extending generative models beyond numerical data to encompass images and speech. Autoencoders, the foundation of VAEs, encode unlabeled data into a compressed form and then decode it back into its original structure. Variational autoencoders introduced the crucial ability to output variations on the original data, paving the way for subsequent models like generative adversarial networks (GANs) and diffusion models, capable of producing realistic yet synthetic content.
Transformers, introduced by Google in 2017, marked another milestone. Combining an encoder-decoder architecture with attention mechanisms, transformers revolutionized how language models were trained. These models, known as foundation models, could be pre-trained on vast amounts of raw text and later fine-tuned for specific tasks with minimal labeled data. The versatility of transformers is evident in their applications for both non-generative tasks like classification and generative tasks such as translation, summarization, and question answering.
Evolution of Generative AI
The progression of generative AI has been marked by a shift towards larger models, exemplified by Google's GPT-3. These models, with their massive parameters, are capable of generating convincing dialogue, essays, and various content types. Language transformers fall into three categories: encoder-only models (e.g., BERT), decoder-only models (e.g., GPT-3), and encoder-decoder models (e.g., Google's Text-to-Text Transfer Transformer, T5).
While the ability to leverage unlabeled data was a crucial innovation, human supervision has staged a comeback. Supervised learning, particularly instruction-tuning, allows generative models to move beyond simple tasks and offer interactive, generalized assistance. The use of prompts, carefully engineered initial inputs, enables customization of models for a wide range of tasks, with the advent of zero-shot and few-shot learning reducing the need for extensive labeled data.
The Role of Human Supervision
Human supervision is shaping generative models by aligning their behavior with human expectations. Reinforcement learning from human feedback (RLHF) has gained prominence, as exemplified by OpenAI's approach in training models like ChatGPT. In RLHF, models output candidate responses rated by humans for correctness, adjusting the model through reinforcement learning to produce high-quality conversational text aligned with human preferences.
The Future of Generative AI
The trajectory of generative AI poses intriguing questions about the scale of models. While there has been a trend towards larger models, recent evidence suggests that smaller, domain-specialized models trained on specific datasets can outperform larger, general-purpose counterparts. The emergence of model distillation, where capabilities of large models are infused into smaller ones, challenges the necessity of massive models for certain applications.
Generative AI holds immense potential for enterprise applications but introduces new challenges, including legal, financial, and reputational risks. Addressing issues like hallucinations, biases, and privacy concerns will be crucial as generative models become integral to diverse industries. As we navigate the evolving landscape of generative AI, a balance between model scale, specialization, and ethical considerations will shape its trajectory.
5G is an important part of the evolution of cloud-computing ecosystems towards more distributed environments, even though it is still many years away from widespread expansion.
Between now and 2025, the networking industry is investing $ 5 trillion globally in 5G, supporting the rapid adoption of mobile, edge, and embedded devices in every sphere of our lives.5G is a major catalyst for the trend, under which more workload is being implemented and data is on edge devices.
AI will play an important part in ensuring that 5G networks are optimized 24 × 7 end-to-end.How 5G Can Use AIAI hybrid lives on every edge of the clouds, multi-cloud, and future mesh networks.
Already, we have seen major AI platform vendors make significant investments in 5G-based services for 5 Mobility, Internet of Things (IoT), and other edge environments.To better understand how 5G empowers the online economy, let’s consider how this emerging wireless architecture delivers value across the AI in telecom toolchain:Next-Generation Edge Convergence with AI Systems on the Chip5G combines digital cellular technology with wireless long-term evolution and Wi-Fi interface.
When deployed on cross-technology network interfaces, 5G allows each edge device to rotate smoothly between indoor and wide-area environments.The adoption of technology someday meets the radio spectra for these asymmetric radio channels and network interfaces to single chips that are active in maintaining seamless connections across multiple radio access technologies.
As a result, 5G has a much faster download and upload speed than 4G: 20 gigabits per second, which is 4 times the rate of 5–12 megabits per second.Most of the application of 5G arises from the ability to transmit data over multiple bitstreams simultaneously between the bandwidth and the transmission capacity connection base station and edge devices.How AI can benefit 5GAI is also a key component of infrastructure to ensure that 5G networks, in all their complexity, can support AI and other application workloads.
Artificial intelligence is surely expected to achieve the unexpected; in every possible way.
Here are some of the everyday applications, which you may have not noticed before.
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Artificial Intelligence is also known as machine intelligence, where machines mimic the intelligence of the human mind and actions.
The primary purpose of artificial intelligence is to make rational decisions according to the circumstances.
The correct implementation of AI can carry more complex tasks with less time.
Artificial intelligence has received an upward trend of growth, leading to the development of human society and multiple industries, and artificial intelligence will continue to grow in promising ways for consumers and various businesses.As 2020 has not been a great year so far because of COVID-19, artificial intelligence has played a massive role in combating the pandemic induced situations.
Every ounce of technological advances and innovations helped us to fight the pandemic.Every organization is finding new ways to operate effectively and to serve their customers as social distancing measures remain in place.
These chatbots have provided the answer to almost every possible question, from evaluation to exercise without putting the human resource at risk.Machine learning and artificial intelligence have helped researchers to analyze a large volume of data to take preventive measures.
There are several ways organizations deal with the contracting process.
Few manage it with word-based drafting and redlining coupled with storing in repositories like Drop Box or SharePoint and some deploy automated contract management systems for this process that also include collaboration.https://blogs.docskiff.ai/top-5-ingredients-of-ai-based-contract-analytics-platforms/
