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How Deep Learning Lets AI Tackle Complex Tasks

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Scientists and engineers are venturing into the realm of artificial intelligence (AI) by employing an innovative method known as deep learning, aimed at imbuing machines with human-like cognitive abilities.
Deep learning draws inspiration from the intricate neural networks found in the human brain. By constructing artificial neural networks with multiple layers, researchers enable computers to autonomously identify patterns in vast datasets and grasp high-level features.
Key components of deep learning include state-of-the-art computer processors, extensive training datasets, and a considerable amount of patience. Deep learning systems require substantial data inputs, ranging from images and videos to speech and text, to develop capabilities such as object recognition, language translation, and decision-making.
Empowering Chatbots
Deep learning represents a subset of machine learning that mirrors the cognitive processes of the human brain in data processing and pattern recognition. It revolves around artificial neural networks, algorithms inspired by the biological structure and function of our brains. These networks comprise layers of interconnected nodes, or “neurons,” each layer dedicated to specific tasks, with data processed as it traverses through these layers.
What distinguishes deep learning from other machine learning techniques is its capacity for automatic feature extraction. This means it can discern intricate patterns and correlations within large datasets without explicit instructions. As the network encounters more data, it adapts and enhances its accuracy over time, refining its decision-making capabilities.
Thanks to deep learning, significant advancements have been made in natural language processing. In 2020, OpenAI unveiled GPT-3, a language model boasting 175 billion parameters, capable of engaging in conversations, answering queries, generating written content, and coding programs based on natural language prompts. Recent models like DeepMind’s Chinchilla and Anthropic’s Claude achieve comparable performance with fewer parameters, making the technology more accessible and practical.
Deep learning also fuels creativity in art and music. Models like DALL-E, Midjourney, and Stable Diffusion can produce realistic images from textual descriptions, enabling users to effortlessly visualize and manipulate concepts. Musicians leverage deep learning tools to compose melodies, harmonies, and sonic textures for their music.
As deep learning systems evolve and tackle real-world tasks, questions emerge regarding the nature of intelligence and cognition. While some researchers perceive these neural networks as sophisticated pattern recognition engines, others speculate they may develop profound understanding and reasoning abilities. Although these systems often yield accurate results, their inner workings remain largely opaque, raising concerns as they assume roles in critical decision-making processes.
Challenges and Prospects
Deep learning faces limitations and challenges, including its reliance on extensive data and computational resources, the potential to perpetuate biases and misinformation from training data, and the absence of common-sense understanding, leading to errors.
Researchers are actively developing techniques to mitigate these challenges, striving to render deep learning more data-efficient, interpretable, and environmentally sustainable. Despite the remaining hurdles, deep learning is considered to be in its early stages, with anticipated breakthroughs in the coming years. As technology continues to transcend domains traditionally considered uniquely human, it is poised to augment and empower us in ways yet to be fully realized.
Source: pymnts.com

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Valmet enters joint venture with Körber to advance the digital offering to the tissue industry

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Valmet and Körber have on May 29, 2024, reached a joint venture agreement to further strengthen FactoryPal, a venture of Körber. FactoryPal is a software developed for tissue converting operations that improves shopfloor manufacturing performance and productivity.
According to the joint venture agreement, Valmet will become the majority shareholder of FactoryPal, and FactoryPal will continue operating as an own legal entity under the existing FactoryPal brand. The addition of FactoryPal will further strengthen Valmet’s offering of advanced Industrial Internet solutions and digital services to support customers in the tissue industry. The joint venture follows Valmet’s acquisition of Körber’s Business Area Tissue that was completed in November 2023.
FactoryPal software empowers tissue mill teams to achieve seamless operations by generating and utilizing high quality data combined with state-of-the-art user experience and advanced artificial intelligence (AI). FactoryPal acts as a trusted co-pilot on the shop floor.
“Valmet has a unique end to end offering for tissue producers, from stock-preparation and tissue machines to rewinders, converting and packaging lines, as well as services and automation systems. We see FactoryPal as an excellent addition that will provide performance optimization, not only for converting operations, but eventually also for the entire tissue making process,” says Petri Rasinmäki, Business Line President, Paper, Valmet.
“By setting up this powerful partnership, FactoryPal will be able to advance its product development with extensive machinery and paper making knowledge. This will empower us to deliver further innovative digital solutions across the entire tissue value chain, evolving the product to one of the leading AI-copilot solutions in the industry,” states Dr. Nadja Hatzijordanou, CEO of FactoryPal.
Currently, there are 55 employees working for FactoryPal in Germany, Portugal, Italy, the USA and Brazil. The set up of the joint venture is subject to customary closing conditions. The closing of the agreement is estimated to occur at earliest on August 1st, 2024.
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EU Policy. AI Office set-up announced, Lucilla Sioli to be in charge

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The Commission has announced the establishment of a new unit tasked with overseeing the implementation of the AI Act.
Lucilla Sioli, currently serving as Director for AI and Digital Industry within the European Commission, will lead the newly formed AI Office, as announced by the EU executive today (29 May). Sioli, an EU official since 1997, brings extensive experience to her new role.
The AI Office, a reorganization of an existing unit dedicated to AI within the Commission, will comprise five main departments, each headed by a director responsible for ensuring compliance with the AI Act. With a total staff of 140, including experts in technology, law, and policy, the office plans to recruit around 80 additional personnel.
These departments will focus on key areas such as regulation and compliance, safety, excellence and robotics, AI for societal good, and innovation. Additionally, two advisors will provide expertise on scientific approaches and international affairs.
Contrary to previous expectations, the EU executive has expedited the approval of internal restructuring, with appointments and unit establishment scheduled to take effect on 16 June.
The AI Office, an internal department within the Commission, will oversee the enforcement of regulations governing general-purpose AI systems and serve as the central coordinating body for AI policy across the EU. It will collaborate with other departments within the EU executive, its agencies, companies, and the 27 Member States.
As the AI Act is set to officially enter into force in June, companies are gearing up to meet compliance deadlines. Prohibited practices specified in the AI Act will face bans starting in November, with general-purpose AI rules coming into effect in May 2025, followed by obligations for high-risk systems in three years.
The AI Board, comprising the 27 national regulators, will convene for its inaugural meeting in June to assist the AI Office in harmonizing regulations. Member states will have twelve months to appoint formal regulatory authorities before attending these initial meetings.
Recruitment for the AI Office is underway, with the Commission seeking experts from national administrations, as well as legal, policy, and technology professionals, through a recent call for applications.
Source: euronews.com
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Oneida County officials discuss artificial intelligence

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Artificial intelligence (AI) has evolved significantly from its early days, prompting local officials and experts to engage with residents of Oneida County to dispel misconceptions and discuss its implications.
At a recent gathering hosted at Mohawk Valley Community College’s Rome Campus, State Sen. Joseph Griffo, R-Rome, and Oneida County Executive Anthony Picente Jr. convened with four experts to offer insights into AI.
The panel included:

Anthony Martino, director of Northeast Cybersecurity and Forensics Center at Utica University,
Charles Green, co-founder and CEO of Assured Information Security, Inc.,
Dr. Andy Drozd, president and chief scientist at ANDRO Computational Solutions,
LLC, and Stephen Acquario, Executive Director of New York State Association of Counties (NYSAC).

Griffo highlighted New York’s ambition to become a hub for AI research and development, stressing the importance of understanding AI’s implications for potential legislation. Picente echoed the sentiment, emphasizing the need to safeguard communities and individuals affected by AI.
The discussion delved into defining AI, with Martino cautioning against the indiscriminate labeling of technology as AI. Drozd described AI as a technology capable of processing vast amounts of data and delivering contextual and useful insights within seconds. Green underscored AI’s historical development and its current applications, particularly in large language models like ChatGPT.
While acknowledging AI’s transformative potential, the panel recognized the importance of data quality, as AI is only as effective as the data it’s fed. They highlighted AI’s role in personalizing online experiences and automating tasks like data analysis for government agencies.
However, concerns were raised regarding data privacy and intellectual property rights, with residents expressing apprehension about data collection and usage by AI systems. Martino and Drozd discussed emerging concepts around personal intellectual property and data ownership.
Regarding AI’s role in education, Drozd highlighted the availability of plagiarism detection services tailored for AI-generated content. Picente shared observations of AI-generated content inaccuracies, emphasizing the importance of monitoring AI applications.
While acknowledging AI’s benefits, Griffo and Picente stressed the need for responsible AI deployment, particularly in government services. They advocated for cautious monitoring of AI advancements and prioritized its use in enhancing operational efficiency at the county and local level.
In conclusion, the discussion underscored the need for informed dialogue and proactive measures to harness AI’s potential while mitigating risks and ensuring equitable access to its benefits.
Source: eu.uticaod.com
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