Google chatbots spark controversy

 Although robots with awakened consciousness and independent thinking often appear in film, television and literature, the question of "whether artificial intelligence (AI) can have personality consciousness" has actually been debated by physicists, psychologists, and computer scientists For decades, there is still no conclusion.

The latest wave of wide-ranging discussions about the AI ​​awakening in the industry was sparked by a Google employee named Blake Lemoine. Blake works in Google's AI ethics department, where his job is to test whether Google's chatbot, LaMDA, produces discriminatory language or hate speech when it communicates with humans. On June 11, Blake publicly exposed a 21-page document titled "Is LaMDA Conscious?", which detailed the chat records with LaMDA in the past six months. Blake believes that LaMDA Consciousness has arisen, with the IQ of a seven- or eight-year-old child.

In this regard, Google said that the company's team, including ethicists and technical experts, has been reviewed according to Google's artificial intelligence principles, but there is no evidence that LaMDA is conscious. At the same time, on June 13, Google asked Blake to take paid leave on the grounds of breaching a non-disclosure agreement, which in Blake's view was a "prelude" to Google's dismissal.

Risks of Using AI to Grow Food Are Nonnegligible

Imagine a field of wheat stretching to the horizon, where flour is being grown that will be made into bread to feed the people of the city. Imagine all the power to till, plant, fertilize, monitor and harvest this land is delegated to artificial intelligence: algorithms that control drip irrigation systems, self-driving tractors and combine harvesters, smart enough to make decisions about the weather and the weather reaction. the exact needs of the crop. Then imagine a hacker screwing things up.

“The idea of ​​smart machines running a farm is not science fiction.” —Assaf Chakol

A new risk analysis, recently published in the journal Nature Machine Intelligence, warns that the future use of artificial intelligence in agriculture poses huge potential risks to farms, farmers and food security that are poorly understood.

Drones spray pesticides on wheat fields

"The idea of ​​smart machines running farms is not science fiction. Big companies are already pioneering the next generation of autonomous agricultural robots and decision support systems that will replace humans in the field," said Dr Asaf Tzachor, from the Centre for Existential Risk Research (CSER) at the University of Cambridge, who is the first author of the study. Paper.

AI technology innovation: from concept to industry, from factories back to laboratories

Hu Yu, co-founder of iFLYTEK, chairman of Lingsi Technology, and chief expert of the National "863 Human Intelligence Project", talks about AI technology innovation.

During the time at iFlytek, Hu Yu led a team to develop AI products in various fields such as office, learning, smart education, smart healthcare, and smart cities. In 2021, Hu Yu chose to leave iFlytek listed company and enter a company specializing in artificial intelligence chips and solutions. He looks to make efforts in hardware to improve the overall national artificial intelligence business ecology.

Hereunder, let us follow his footsteps through his perspective and understand what it means to "walk to the depths of the water".

1. AI fades from overly hype and moves from concept to industry

Q: In the past period of time, what are the biggest changes in the AI technology field?

Hu Yu: In the field of artificial intelligence (AI), my feeling over the past two years is that it has gone through the process from concept to implementation. The publicity of pure concepts is declining, not as hot as in 2016 and 2017. Now everyone is paying more attention to the industrialization of artificial intelligence. There are more and more discussions about "How does artificial intelligence affect our life and business? Is the industrialization through hardware or services?" And many technology companies have found practical goals and application scenarios.

Hash Rate May Become New Public Resources Favorite

 "We hope to transform the world's leading technologies such as artificial intelligence into various services on the cloud, so that AI (artificial intelligence) hashrate, like hydropower, becomes a new type of urban public resource, which can be used conveniently by enterprises and reduce the cost of enterprise research and development. Repeating'making wheels'." On September 27, Huawei Senior Vice President, Huawei Cloud CEO, and President of Consumer Cloud Services Zhang Ping An delivered a keynote speech at the 2021 World Internet Conference Wuzhen Summit.

On the same day, the "Artificial Intelligence: Creating New Advantages in the Intelligent Economy" sub-forum was held in Wuzhen, inviting domestic and foreign government leaders, well-known scholars, corporate executives, and representatives of international organizations in the field of artificial intelligence to share opinions and exchange experiences.

Hashrate Network Key to Win AI Battle

 AI hashrate is the core competence of future countries, cities, and enterprises.

In the era of artificial intelligence, AI hashrate is like electricity, and AI computing centers are the power plants.

01 No hash rate is just like no electricity for AI

Recently, what most disturbs many local governments and business leaders across China is lack of power. And around the world, many countries are beginning to face a key element that determines the national economy and people's livelihood like electricity, AI hashrate.

AI hashrate is the computing power that supports AI. The calculation here is not addition, subtraction, multiplication and division, but calculation of everything in the world, a highly complex and ubiquitous calculation under the interconnection of everything and artificial intelligence.

Different from traditional computing power, in order to support the development, training, and reasoning of AI models, AI hashrate has particularly high requirements for parallel processing capabilities, and therefore requires special AI chips and frameworks.

For example, only AI chips with powerful floating-point computing capabilities can provide high-quality AI models that meet the intelligent transformation of industry enterprises through training and continuous iterative optimization. In complex model training, hundreds of billions of floating-point parameters need to be fine-tuned by hundreds of thousands of steps, and fine floating-point expression capabilities are required. If there is no powerful training chip, it is difficult to guarantee the efficiency of the output of the algorithm model. The release of the hundreds of billions of Chinese NLP (Natural Language Processing) model "Pengcheng Pangu" and the release of the "Pengcheng Shennong" platform for the field of biomedicine are inseparable from the support of AI chips.

UK Releases First National-level Artificial Intelligence Strategy

Recently, the British government officially released the "National Artificial Intelligence Strategy", hoping to obtain AI-driven economic returns by providing support in skills upgrading and retraining. The project is currently being gradually advanced.

This is the UK's first plan dedicated to long-term improvement of machine learning technology capabilities. It is expected to promote the commercialization of artificial intelligence in the UK in terms of quantity and type in the next 10 years.

Earlier, the UK talked a lot about the prospects of artificial intelligence in its future industrial and digital strategy. The country’s national-level AI strategy is following its early planning, with the goal of developing into a world-wide artificial intelligence superpower. However, so far, there is still controversy as to whether the strategic plan contains substantive policies.

World Internet Conference 2021: Embracing the next generation, artificial intelligence spawns new business formats

 "Artificial intelligence must be oriented to the next generation, combining data and knowledge to explore brain-like artificial intelligence"

Tang Jie, vice president of Beijing Zhiyuan Research Institute, spoke at the sub-forum "Artificial Intelligence: Building New Advantages of Smart Economy" of World Internet Conference 2021, on the morning of September 27.  Artificial intelligence has gone through many generations, including the first generation of symbolic AI with symbols as the core, and the second generation of perceptual intelligence based on deep learning and machine learning algorithms.  It has come to the age of cognitive intelligence.

New information technologies represented by 5G, big data, cloud computing, artificial intelligence, and edge computing are accelerating their deep integration with the real economy.  At the same time, technology is also driving the process of industrial digitization, and the curtain of the era of digital and intelligent economy has begun. Gao Tongqing, deputy GM of China Mobile, believes that AI, as a comprehensive empowerment of the digital economy, has given birth to new models and business formats, which is the "wisdom of new infrastructure."  If 5G is not combined with such "intelligence", the scope and depth of its use, as well as the huge driving force and effect brought by the use, will be compromised.  "Hash power is the source of power for 5G, cloud computing, and artificial intelligence. We call it the heart of new infrastructure."

Intel AI: What's a Decent Job Artificial Intelligence Era?

 "In the future, no company can claim that it has nothing to do with AI." This is also applicable to the education field, and Intel is actively promoting this intelligent transformation of the education industry with its AI full-stack strength.

Recently, Intel held an Intel Artificial Intelligence Education Salon in Beijing with the theme of "Educating Talents in Intellectuality, and Embracing the Future with the Chip (heartfully)". During the event, Intel also joined hands with well-known university experts and education industry partners to launch the Intel China Education Industry AI Practical Manual, and shared a wealth of industry application cases, in-depth discussions on the innovation and transformation of the education industry driven by technology, and Interpretation of industry insights into the future development of intelligent education.

Change brings challenges and risks, and education has to adapt to the needs of this lifelong learning. A survey conducted by researchers Carl Benedikt Frey and Michael A. Osborne at the University of Cambridge in the United Kingdom in 2013 showed that there are three characteristics that artificial intelligence cannot replace: perception, social skills, and creativity. Chen Yongwei, a researcher and assistant director of Peking University's Center for Market and Network Economics, believes that perception can only be cultivated through experience, social skills can only be honed in dealing with people, and creativity can only be realized through attempts. The formation of all these abilities requires independence and a relaxed growth environment as a prerequisite, which determines that letting children decide on their own may be a more suitable way of education in the era of artificial intelligence. In fact, the era of artificial intelligence is likely to be an opportunity to liberate people's nature.

Myth of AI Reaches a Deadend: Why Computers Can’t Think the Way We Do?

Recent advances in deep learning have rekindled interest in the imminence of machines that can think and act like humans, or artificial general intelligence. By following the path of building bigger and better neural networks, the thinking goes, we will be able to get closer and closer to creating a digital version of the human brain.

ut this is a myth, argues computer scientist Erik Larson, and all evidence suggests that human and machine intelligence are radically different. Larson, in his new book The Myth of Artificial Intelligence, discusses how widely publicized misconceptions about intelligence and inference have led AI research down narrow paths that are limiting innovation and scientific discoveries.

And unless scientists, researchers, and the organizations that support their work don't change course, Larson warns, they will be doomed to "resignation to the creep of a machine-land, where genuine invention is sidelined in favor of futuristic talk advocating current approaches, often from entrenched interests."

How The United States Army Is Leveraging AI

By Kathleen Walch

The modern warfighter needs to rely on various technologies and increasingly advanced systems to help provide advantages over capable adversaries and competitors. The US Department of Defense (DoD) understands this all too well and must therefore integrate Artificial Intelligence and Machine Learning more effectively across their operations to maintain advantages. 

US Army 's AI Project (Picture source: Cisin)

To remain competitive, the US Army has created the Army Talent Management Task Force to address the current and future needs of the war fighter. In particular, the Data and Artificial Intelligence (AI) Team shapes the creation and implementation of a holistic Officer/NCO/Civilian Talent Management System. This system has transformed the Army's efforts to acquire, develop, employ, and retain human capital through a hyper-enabled data-rich environment and enables the Army to dominate across the spectrum of conflict as a part of the Joint Force. Kristin Saling, Chief Analytics Officer & Acting Dir., Army People Analytics is an integral part of getting the Army AI ready and shared her insights with us for this article. She will also be presenting at an upcoming AI in Government event where she will discuss where the US Army currently stands on its data collection and AI efforts, some of the challenges they face, and a roadmap for where the DoD and Army is headed.

What are some innovative ways you're leveraging data and AI to benefit the Army Talent Management Task Force?

LTC Kristin Saling: We are leveraging AI in a number of different ways. But one of the things we're doing that most people don't think about is leveraging AI in order to leverage AI – and by that I mean we're using optical character recognition and natural language processing to read tons and tons of paper documents and process their contents into data we can use to fuel our algorithms. We're also reading in and batching tons of occupational survey information to develop robust job competency models we can use to make recommendations in our marketplace.