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Alibaba’s top 10 2020 tech trends

By Ben Moore
Tue 14 Jan 2020
FYI, this story is more than a year old

In 2020 it is guaranteed that there will be a variety of technological advancements and breakthroughs that will impact our everyday lives. 

In recent times, the world has seen the emergence of cognitive intelligence, in-memory-computing, fault-tolerant quantum computing and new materials-based semiconductor devices. 

We have also witnessed faster growth in industrial IoT, large-scale collaboration between machines, production-grade blockchain applications, modular chip design and AI technologies to protect data privacy.

“We are in an era of rapid technology development. In particular, technologies such as cloud computing, artificial intelligence, blockchain, and data intelligence are expected to accelerate the pace of the digital economy,” says Alibaba cloud intelligence president and DAMO Academy head Jeff Zhang.

“In addition to exploring the unknown through scientific and technological research, we are also working with industry players to foster innovation in different industries, making technologies more accessible for businesses and society at large.”                                             

Here are the highlights from the Academy’s predicted 2020 trends in the tech community:

        1. Evolving Artificial intelligence from perceptual intelligence to cognitive intelligence

Currently, artificial intelligence has surpassed humans in perceptual intelligence. However, cognitively, AI still operates at an infant level. 

Cognitive intelligence refers to modes like external knowledge, logical reasoning or domain migration. 

To help further AI’s progress in this field, computer scientists will draw inspiration from cognitive psychology, brain science and human social history. 

It is expected that these components combined with techniques such as cross-domain knowledge graph, causality inference and continuous learning, will generate effective mechanisms for stable acquisition and expression of knowledge. 

The ability to understand and utilise knowledge is likely to be the next big breakthrough in AI.

        2. In-Memory-Computing overcomes the "memory wall" challenges in AI computing

In Von Neumann architecture, memory and processors are separate, meaning the functioning requires data to move back and forth. 

Due to the rapid development of data-driven AI algorithms, hardware has now become the bottleneck in the exploration of more advanced algorithms.

Contrastly, in processing-in-memory (PIM) architecture, the memory and processors are fused together and calculations are performed while data is stored for minimal movement. By using this process, computation parallelism and power efficiency can be significantly improved. 

Alibaba says that PIM architecture innovation is the tickets to next-generation AI. 

        3. Industrial IoT powers digital transformations 

It is expected that the rapid development of 5G, IoT devices, cloud computing and edge computing will accelerate the fusion of information systems, communication systems and industrial control systems. 

Advancing Industrial IoT means manufacturing companies can achieve automation of machines, in-factory logistics, and production scheduling, as a way to realise C2B smart manufacturing. 

Also, interconnected industrial systems can adjust and coordinate the production capability of both upstream and downstream vendors.

Ultimately this will significantly increase the manufacturers’ productivity and profitability. For instance, if productivity increases 5-10% for a manufacturer with production goods that value hundreds of trillion RMB, they will see trillions more RMB.

        4. Large-scale collaboration between machines become possible

The traditionally singularly intelligent machine cannot meet the real-time perception and decision making of large-scale intelligent devices. 

However, the development of collaborative sensing technology of the Internet of things and 5G communication technology allows us to collaborate among multiple agents and overcome this flaw. 

This will see machines cooperate with other machines and compete with each other to complete the assigned tasks. 

Grouped intelligence brought by the cooperation of multiple intelligent bodies will further amplify the value of the intelligent system. 

Leading to large-scale intelligent traffic light dispatching, realising dynamic and real-time adjustment, while warehouse robots will work together to complete cargo sorting more efficiently. 

Regarding delivery, driverless cars will also be able to perceive the overall traffic conditions on the road and group unmanned aerial vehicles (UAV) to achieve optimum efficiency.

        5. Modular design makes chips easier and faster by stacking chiplets together

The traditional model of chip design cannot efficiently respond to the fast-evolving, fragmented and customised needs of chip production. 

The open source SoC chip design based on RISC-V, high-level hardware description language and IP-based modular chip design methods have accelerated the rapid development of agile design methods and the ecosystem of open source chips. 

Also, the modular design method based on chiplets uses advanced packaging methods to package the chiplets with different functions together, which can quickly customise and deliver chips that meet specific requirements of different applications.

        6. Large-scale production-grade blockchain applications will gain mass adoption 

BaaS (Blockchain-as-a-Service) will further reduce the barriers of entry for enterprise blockchain applications. 

A variety of hardware chips embedded with core algorithms used in edge, cloud and designed specifically for blockchain will also emerge. 

This will allow assets in the physical world to be mapped to resources on blockchain, further expanding the boundaries of the Internet of Value and realising "multi-chain interconnection". 

In the future, we will see mass adoption of a large number of innovative blockchain application scenarios with multi-dimensional collaboration across different industries and ecosystems, as well as large-scale production-grade blockchain applications with more than 10 million DAI (Daily Active Items). 

        7. A critical period before large-scale quantum computing

In 2019, we saw a race in reaching “Quantum Supremacy”, turning the tech industry’s attention back to quantum computing. 

Last year’s use of superconducting circuits boosted the overall confidence for superconducting quantum computing to realise large-scale quantum computers.

In 2020, the field of quantum computing will receive increasing investment, which comes with greater competition. The field is also expected to experience a speed-up in industrialisation and the gradual formation of an eco-system.

In the coming years, the next milestones will be the realisation of fault-tolerant quantum computing and the demonstration of quantum advantages in real-world problems. 

Given the present knowledge, both these advancements will prove to be a major challenge, meaning 2020 is a critical period for Quantum computing.

        8. New materials will revolutionise the semiconductor devices

Under the pressure of both Moore's Law and the explosive demand for computing power and storage, it is difficult for classic Si-based transistors to maintain sustainable development in the semiconductor industry. Until now, major semiconductor manufacturers had no clear answer and chip options beyond the 3nm. 

However, new materials can now drive continuous innovation in the semiconductor industry. This involves utilising new logic, storage and interconnection devices through new physical mechanisms. 

For example, topological insulators and two-dimensional superconducting materials that can achieve lossless transport of electron and spin can become the basis for new high-performance logic and interconnection devices. 

While new magnetic materials and new resistive switching materials can realise high-performance magnetics memory such as SOT-MRAM and resistive memory.

        9. Growing adoption of AI technologies that protect data privacy

The compliance costs demanded by the recent data protection laws and regulations related to data transfer are getting higher than ever before. 

In light of this, there have been growing interests in using AI technologies to protect data privacy. 

The objective is to enable the data user to compute a function over input data from different data providers while keeping their data private. 

Such AI technologies promise to solve the problems of data silos and lack of trust in today's data-sharing practices, as well as truly unleash the value of data in the foreseeable future.

        10. Cloud becomes the centre of IT technology innovation

With the ongoing development of cloud computing technology, cloud has grown far beyond the scope of IT infrastructure, gradually evolving into the centre of all IT technology innovations. 

Cloud has a close relationship with almost all IT technologies, including new chips, new databases, self-driving adaptive networks, big data, AI, IoT, blockchain, quantum computing and so forth. 

Meanwhile, it creates new technologies, such as serverless computing, cloud-native software architecture, software-hardware integrated design, as well as intelligent automated operation. 

Through these capabilities cloud computing is redefining every aspect of IT, making new IT technologies more accessible to the public. Cloud has become the backbone of the entire digital economy.

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