印度凭借神经形态计算的突破成为全球人工智能竞赛的关键参与者

印度快速发展的人工智能产业也得到了政府的大力支持，政府出台了多项战略政策

Artificial Intelligence (AI) is rapidly changing the world, becoming an integral part of businesses across various sectors. From healthcare, retail, finance, manufacturing, and supply chain to education, energy, and entertainment industries, there has been a growing demand for AI-powered tools.

人工智能 (AI) 正在迅速改变世界，成为各行业企业不可或缺的一部分。从医疗保健、零售、金融、制造和供应链到教育、能源和娱乐行业，对人工智能工具的需求不断增长。

According to PWC, AI can transform the productivity and GDP potential of the global economy with the greatest gains to be seen in China and North America, equivalent to a total of $10.7 trillion.

普华永道表示，人工智能可以改变全球经济的生产力和 GDP 潜力，其中中国和北美的收益最大，总计相当于 10.7 万亿美元。

Driven by this growing usage, the AI market is expected to grow from $50 bln in 2023 to well above $800 bln by the end of this decade, as per Statista.

根据 Statista 的数据，在这种不断增长的使用量的推动下，人工智能市场预计将从 2023 年的 500 亿美元增长到本十年末的 8000 亿美元以上。

This AI boom has led to competition between large companies to develop the most powerful AI models worldwide, and countries are keen to foster their own competing AI systems.

人工智能的热潮引发了大公司之间的竞争，以开发全球最强大的人工智能模型，各国都热衷于培育自己的竞争性人工智能系统。

Amidst this, India has emerged as a key player, with 92% of knowledge workers utilizing generative AI compared to the much lower global average of 75%.

其中，印度已成为关键参与者，92% 的知识工作者使用生成式人工智能，而全球平均水平则低得多（75%）。

Just late last month, Asia's richest man, Mukesh Ambani, chairman of Reliance Industries, unveiled “JioBrain,” a suite of AI tools and applications to transform businesses in energy, textiles, and more. Reliance's telecommunications business is currently working with the Indian Institute of Technology (IIT) to launch “Bharat GPT” for Indian users.

就在上个月末，亚洲首富、信实工业公司董事长穆克什·安巴尼 (Mukesh Ambani) 推出了“JioBrain”，这是一套人工智能工具和应用程序，旨在改变能源、纺织等领域的业务。 Reliance的电信业务目前正在与印度理工学院（IIT）合作，为印度用户推出“Bharat GPT”。

“We need to be at the forefront of using data, with AI as an enabler for achieving a quantum jump in productivity and efficiency. “

“我们需要站在数据使用的最前沿，以人工智能为推动力，实现生产力和效率的飞跃。 “

– Mukesh Ambani

——穆克什·安巴尼

After building a high-powered IT industry worth $250 bln, India is now setting its eyes on AI services, which, according to a report by Nasscom and BCG, could be worth $17 bln in the next three years.

在建立了价值 2500 亿美元的强大 IT 产业后，印度现在将目光投向人工智能服务，根据 Nasscom 和 BCG 的一份报告，未来三年该服务的价值可能达到 170 亿美元。

With over 900 million internet users, India has emerged as “the data capital of the world.” The fact that so much data is publicly available is extremely beneficial for companies, as they can write their own AI algorithms.

印度拥有超过 9 亿互联网用户，已成为“世界数据之都”。事实上，如此多的数据是公开的，这对公司来说非常有利，因为他们可以编写自己的人工智能算法。

However, computing power and shared resources are needed to accelerate the country's AI industry. For this, the Indian government has procured a thousand GPUs to offer computing capacity to AI makers.

然而，加速国家人工智能产业发展需要计算能力和共享资源。为此，印度政府采购了一千块GPU，为人工智能制造商提供计算能力。

Earlier this year, the first shipment of Nvidia chips arrived in Indian data centers after the CEO of the world's largest chipmaker, Jensen Huang, visited India and had a discussion with Prime Minister Narendra Modi and tech executives.

今年早些时候，全球最大芯片制造商首席执行官黄仁勋访问印度并与总理纳伦德拉·莫迪和科技高管进行讨论后，第一批英伟达芯片抵达印度数据中心。

“You have the data, you have the talent. This is going to be one of the largest AI markets in the world.”

“你有数据，你有才华。这将成为世界上最大的人工智能市场之一。”

– Huang told the PM at the time

– 黄当时告诉总理

A Breakthrough: Mimicking the Brain for Smarter Computing

突破：模仿大脑实现更智能的计算

Amidst all this, scientists at the Centre for Nano Science and Engineering (CeNSe), Indian Institute of Science (IISc), Bangalore, India, made a major breakthrough in neuromorphic computing technology. This technology mimics the human brain's structure and function to create more efficient and intelligent computing systems.

其中，印度班加罗尔印度科学研究所（IISc）纳米科学与工程中心（CeNSe）的科学家在神经形态计算技术方面取得了重大突破。该技术模仿人脑的结构和功能，以创建更高效​​、更智能的计算系统。

This momentous progress can help India become a major player in the global AI race and make AI computing accessible to everyone and integrated into their personal devices.

这一重大进展可以帮助印度成为全球人工智能竞赛的主要参与者，并使人工智能计算可供每个人使用并集成到他们的个人设备中。

This is certainly a great feat, given that the conventional ‘cloud computing model' requires large data centers that consume a lot of energy. Using resource-intensive data centers limits their use to a small community of developers.

考虑到传统的“云计算模型”需要消耗大量能源的大型数据中心，这无疑是一项伟大的壮举。使用资源密集型数据中心将其使用限制在小型开发人员社区中。

Neuromorphic hardware promises enhanced energy efficiency and space for AI. However, at present, it can only handle low-accuracy operations. Tasks like NLP, neural network training, and signal processing require substantially more computing resolution and are currently beyond the scope of individual neuromorphic circuit elements.

神经形态硬件有望提高人工智能的能源效率和空间。但目前只能处理低精度运算。 NLP、神经网络训练和信号处理等任务需要更高的计算分辨率，目前超出了单个神经形态电路元件的范围。

However, the latest advancement by IISc scientists can actually help with it and move the space towards' edge computing,' which moves data processing and storage closer to devices that create and use the data. This reduces latency, improves application performance, and saves network costs.

然而，IISc 科学家的最新进展实际上可以对此有所帮助，并将空间推向“边缘计算”，从而使数据处理和存储更接近创建和使用数据的设备。这可以减少延迟、提高应用程序性能并节省网络成本。

Edge computing further enables real-time applications, as well as AI and machine learning applications, to process large volumes of data with greater speed and reliability.

边缘计算进一步使实时应用程序以及人工智能和机器学习应用程序能够以更快的速度和可靠性处理大量数据。

Published in Nature, the latest research by Professor Sreetosh Goswami at the CeNSe, IISC, who led a group of scientists and students, developed a type of semiconductor device called Memristor. Instead of using traditional silicon-based technology, Memristor was created using a metal-organic film.

IISC 领导一组科学家和学生的 CeNSe 教授 Sreetosh Goswami 的最新研究发表在《自然》杂志上，开发了一种名为忆阻器的半导体器件。忆阻器不是使用传统的硅基技术，而是使用金属有机薄膜制造。

The use of molecular films allowed researchers to track free ionic movements, which widened the memory pathways.

分子薄膜的使用使研究人员能够追踪自由离子的运动，从而拓宽了记忆途径。

To establish kinetic controls over the molecular transition that enabled the neuromorphic traits in a single circuit element, scientists applied voltage pulses and then mapped molecular movements to a distinct electrical signal. This created an extensive ‘molecular diary' of different states.

为了建立对分子转变的动力学控制，从而在单个电路元件中实现神经形态特征，科学家们施加电压脉冲，然后将分子运动映射到不同的电信号。这创造了不同状态的广泛“分子日记”。

“Due to this free ionic movement, countless unique memory states and pathways were generated. Such intermediary states had remained inaccessible, so far, as most digital devices are only able to access two either high and low conductance states.”

“由于这种自由离子运动，产生了无数独特的记忆状态和路径。到目前为止，这种中间状态仍然无法访问，因为大多数数字设备只能访问高电导状态和低电导状态。”

– Professor Sreebrata Goswami, father of Prof Sreetosh

– Sreebrata Goswami 教授，Sreetosh 教授的父亲