Archive for the ‘Artificial Intelligence’ Category
Chinas lawmakers walk fine line between AI development and tighter regulation – South China Morning Post
We must establish a unified market for computing power services and the effective use of resources across the country, Yu, a CPPCC member, said.
Xi Jinpings hi-tech push steals the spotlight at Chinas two sessions
His appeal resonated with other delegates, including telecoms equipment maker ZTEs senior vice-president Miao Wei and Ma Kui, general manager at China Mobiles Sichuan branch, who both called for increased investment in and more coordinated development of computing infrastructure. Miao and Ma are NPC delegates.
Computing power has become the focus of international competition, said Ma, who also highlighted the imbalance of the Chinese AI industry, with research teams located mostly in first -tier cities such as Beijing and Shanghai but computing resources clustered in other smaller cities.
The calls for a state-orchestrated computing infrastructure come after five Chinese government bodies, including MIIT and the National Development and Reform Commission, the countrys top economic planner, issued a policy titled East-West Compute Transfer to coordinate computing resources between Chinas eastern and coastal provinces and its western inland regions.
But Zhang Yunquan, a CPPCC member and a research fellow from the Chinese Academy of Sciences, said the project would not help efforts to train large language (LLM) models for AI, as it mainly serves traditional data centre and cloud computing demands.
Instead, Zhang proposed state-led efforts to coordinate academic and industrial resources to build up a sovereign LLM.
Cao Peng, chair of the technology committee at Chinese e-commerce giant JD.com and head of its cloud unit, called for the development of home-made AI chips to circumvent Washingtons export controls.
Two sessions 2024: Chinas construction of particle collider may start in 2027
Liu Qingfeng, chairman at iFlyTek, a Chinese AI specialist known for its voice recognition capability, called for a national-level approach to systematically and rapidly propel our countrys artificial general intelligence growth.
We need to acknowledge the gap and consolidate resources from the state level to accelerate the catch-up [with US AI firms], according to Liu.
Zeng Yi, a CPPCC member and head of China Electronics Corporation, warned that China was lagging in generative AI when it came to talent and basic scientific research. We are all very anxious about being left behind, Zeng said.
Premier Li Qiang introduced an AI+ initiative to integrate the power of AI across traditional sectors to drive economic growth, and to push for technology upgrades. Meanwhile, Chinas lawmakers and political advisers voiced concern about potential disruptions from AI, and called for effective regulation.
Lou Xiangping, head of China Mobiles branch in the central Henan province, proposed an accountability system to hold service providers such as operators of local ChatGPT-like services responsible for possible mishaps.
China has already implemented a registration system that requires local LLMs to apply for approval before providing public services. More than 40, or around one-fifth of the countrys total number of LLMs, have been given the green light for public release.
Zhang Yi, a CPPCC member and senior partner at law firm King & Wood Mallesons, tabled his proposal about improving AI regulation but also cautioned that too many laws might hinder the development of the local industry.
In explaining his proposal to local media, Zhang said China needs to balance regulation and development through an approach that clearly defines what is illegal, while also allowing companies to innovate and explore new areas.
As global AI competition intensifies [we] need to be wary of how overbearing legal intervention could inhibit the healthy and orderly development of AI, he said.
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Chinas lawmakers walk fine line between AI development and tighter regulation - South China Morning Post
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The benefits and risks of Artificial Intelligence – IT Brief Australia
In little more than 12 months, generative AI has evolved from being a technical novelty into a powerful business tool. However senior IT managers believe the technology brings with it risks as well as benefits.
According to the Immuta 2024 State of Data Security Report, 88% of senior managers say their staff are already using AI tools, regardless of whether their organisation has a firm policy of adoption.
Asked to nominate the key IT security benefits offered by AI, respondents to the Immuta survey pointed to improved phishing attack identification and threat simulation as two of the biggest. Others included anomaly detection and better audits and reporting.
When it came to identifying AI-related risks, inadvertent exposure of sensitive information by employees and unauthorised use of purpose-built models out of context were nominated by respondents. Additional named risks included the inadvertent exposure of sensitive data by large language models (LLOMs) and the poisoning of training data.
Continuing growth Despite these concerns, organisational uptake of AI appears likely to remain brisk. Analyst firm Gartner predicts that IT spending will increase more than 70% during the next year, and a significant portion will be invested in AI-related technologies and tools. Organisations will need to continue to embrace this new technology to remain competitive and relevant in todays economic landscape.
Its likely that 2024 will also become the year of the AI control system. Aside from the hype surrounding generative AI, there is a broader issue around developing a control system for the technology. This is because AI brings an entirely new paradigm where there is little or no human control. AI initiatives, therefore, wont get into full-scale production without a new form of control system in place.
At the same time, organisations will come to realise that, as AI usage increases, they need to focus even more attention on data security. As we have seen with governments around the world, there has also been an urgent need to enact news laws and regulations to ensure that data privacy and data security concerns with generative AI are addressed.
As the technology evolves, it will become clear that the key to harnessing the power of large-language model (LLM)-based AI lies in having a robust data governance framework. Such a framework is essential not only for guiding the ethical and secure use of LLMs but also for establishing standards for measuring their outputs and ensuring integrity.
The evolution of LLMs will open new avenues for applications in data analysis, customer service, and decision-making processes, further embedding LLMs into the fabric of data-driven industries.
The biggest winners when it comes to AI usage will be the organisations that create real value from better data engineering processes that are used to leverage models using their own data and business context. The key impact for these companies will be better knowledge management.
An ongoing reprioritisation and reassignment of resources With the pace of change in technology and data usage likely to continue to increase, organisations will be forced to redirect resources into new data-related areas that will become priorities. Examples include data governance and compliance, data quality, and data integration.
Despite ongoing pressure to do more with less, organisations cant and wont halt investment in IT. These investments will be focussed on the critical building blocks that form the foundation of a modern data stack that is required to support AI initiatives.
Also, the traditional demarcation between data and application layers in an IT infrastructure will be replaced by a more integrated approach focused on data products. Rather than a few dozen apps, there will be hundreds of data products. Dubbed a data-centric architecture, this approach will allow organisations to extract greater value from their data resources and better support their operations.
By working closer to the data, data teams can reduce latency and improve performance, opening up new possibilities for real-time reporting and analytics. This, in turn, supports better decision-making and more efficient business processes.
The coming year will see some fundamental changes in the way businesses manage and work with AI and data. Those that take time to experiment with the technology and determine its best use cases will be best placed to extract maximum value and achieve optimal results.
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The benefits and risks of Artificial Intelligence - IT Brief Australia
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The Bull Market Is Official: 1 Superb Artificial Intelligence (AI) Growth Stock to Buy Before the Nasdaq Soars Higher in … – The Motley Fool
The Bull Market Is Official: 1 Superb Artificial Intelligence (AI) Growth Stock to Buy Before the Nasdaq Soars Higher in ... The Motley Fool
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The Bull Market Is Official: 1 Superb Artificial Intelligence (AI) Growth Stock to Buy Before the Nasdaq Soars Higher in ... - The Motley Fool
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Learn the ways of machine learning with Python through one of these 5 courses and specializations – Fortune
The fastest growing jobs in the world right now are ones dealing with AI and machine learning. Thats according to the World Economic Forum.
This should come at no surprise as new technology is being deployed practically on the daily that is revolutionizing the ways in which the globe works through automation and machine intelligence.
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Beyond having foundational skills in mathematics and computer science and soft skills like problem-solving and communication, core to the AI and machine learning space is programmingspecifically Python. The programming language is one of the most in-demand for all tech experts.
Python plays an integral part of machine learning specialists everyday tasks, says Ratinder Paul Singh Ahuja, CTO and VP at Pure Storage. He specifically points its diverse set of libraries and their relevant roles:
As you can imagine, the best practices in the everchanging AI may differ depending on the day, task, and company. So, building foundational skills overalland being able to differentiate yourselfis important in the space.
The good news for those who are looking to learn the ropes in the machine learning and Python space, there are seemingly endless ways to gain knowledge onlineand even for free.
For those exploring the subject on your own, resources like W3Schools, Kaggle, and Googles crash course are good options. Even as simple as watching YouTube videos and checking out GitHub can be useful.
I think if you focus on core technical skills, and also the ability to differentiate, I think that theres still plenty of opportunity for AI enthusiasts to get into the market, says Rakesh Anigundi, Ryzen AI product lead at AMD.
Anigundi adds that because the field and job market is so complicated, even companies themselves are trying to figure out what are the most useful skills to build products and solve problems. So, doing anything you can to stay ahead of the game can be part of what helps propel your career.
For those looking for a little bit of a deeper dive into machine learning with Python, Fortune has listed some of the options on the market; theyre largely self-paced but vary slightly in terms of price and length.
Participants can watch hours of free videos about machine learning. At the end, each course has one learning multiple-choice question. Users are provided five different challenges to take on. The interactive projects include the creation of a book recommendation engine, neural network SMS text classifier, and cat and dog image classifier.
Cost: Free
Length: Self-paced; 36 lessons + 5 projects
Course examples: Tensorflow; Deep Learning Demystified
Hosted with edX, this introductory course allows students to learn about machine learning and AI straight from two of Harvards expert computer science professors. Participants are exposed to topics like algorithms, neutral networks, and natural language processing. Video transcripts are also notably available in nearly a dozen other languages. For those wanting to learn more, the course is part of Harvards computer science for artificial intelligence professional certificate program.
Cost: Free (certificate available for $299)
Length: 6 weeks (45 hours/week)
Course learning goals: Explore advanced data science; train models; examine result; recognize data bias
Data scientists from IBM guide students through machine learning algorithms, Python classifications techniques, and data regressions. Participants are recommended to have a working knowledge of Python, data analysis, and data visualization as well as high school-level mathematics.
Cost: $49/month
Length: 12 hours (approximately)
Module examples: Regression; Classification; Clustering
With nearly 100 hours of content, instructors from Stanford University and DeepLearning.ai, including renowned AI and edtech leader Andrew Ng, walk students through the foundations of machine learning. It also focuses on the applications of AI into the real world, especially Silicon Valley. Participants are recommended to have some basic coding experience with knowledge of high school-level mathematics.
Cost: $49/month
Length: 2 months (10 hours/week)
Course examples: Supervised Machine Learning: Regression and Classification; Advanced Learning Algorithms; Unsupervised Learning, Recommenders, Reinforcement Learning
A professor from the University of Michigans school of information and college of engineering teaches students the ins and outs of machine learning, with discussion of regressions, classifications, neural networks, and more. The course is for individuals with already some existing knowledge in the data and AI world. It is part of a larger specialization focused on data science methods and techniques.
Cost: $49/month
Length: 31 hours (approximately)
Course examples: Fundamentals of Machine Learning; Supervised Machine Learning; Evaluation
Check out all ofFortunesrankings of degree programs, and learn more about specificcareer paths.
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