Chinese ride-hailing company DiDi Global is collaborating with Chinese carmakers to develop robotaxis. DiDi is developing the robotaxis with multiple car manufacturers, including electric vehicle makers, and is aiming to begin putting them into service by 2025. The company plans for the new vehicles to be domestically produced, with controllable supply chains and key components that are produced nationally. A robotaxi concept was showcased by DiDi called Neuron which featured robotic arms to help passengers pick up luggage. The vehicle had no driver's seat to maximise space for passengers. DiDi began developing and testing autonomous driving vehicles in 2016 and has raised hundreds of millions of dollars in investment from firms such as IDG Capital and Guotai Junan.

We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 – 28. The public cloud is growing rapidly and the market for the technology is expected to reach $1.3 trillion by 2025. The cloud has revolutionized the computing industry and enabled many applications, business models and enterprises, which otherwise wouldn't have been possible. Immediate availability, scalability, minimal capital expenditure and streamlined developer experience are its main advantages -- but it comes at a cost. Due to a lack of in-house infrastructure optimization capabilities, most enterprises stick to the cloud even after achieving certain maturity. To keep cloud spending under control, enterprises have built or acquired tools and services.

The metaverse as described to us by science fiction is a world of infinite possibilities. The easiest way to conceptualise it is by looking at Hollywood blockbusters such as Avatar and Ready, Player One. In the movies, the metaverse is a three-dimensional digital universe where players can escape physical reality, engage with each other as an avatar of their creation and experience anything they want, only limited by the human imagination and technology, says Selina Yuan, general manager of international business unit, Alibaba Cloud Intelligence. Apart from being a wondrous twin digital reality of our physical world, the metaverse's true potential lies in its ability to make better use of the digital intelligence we are already gaining and visualising it in a way that uncovers new insights that might have otherwise remain hidden. This could be the key to helping us solve real-world problems and building a greener, more inclusive, and technically advanced world.

Increase in the use of AI for IoT security – AI helps safeguard assets, reduce fraud, support analytics, and enable automated decision making in IoT applications. Machine learning can be used to monitor incoming and outgoing traffic in IoT devices to create a profile that determines the normal behavior of the IoT ecosystem – helping detect threats via unusual behavior patterns.Moreover, using AI to collect data from smart homes and organizations, web cameras, and other IoT devices helps provide data security and strengthen privacy, reducing the chances of cyberattacks.For example, organizations are using AI to determine employees access patterns, get insights for future office layouts, and detect suspicious activities. Aerospace and defense companies are combining IoT, AI, and cloud infrastructure to discover DoS or DDoS attacks. Taking a network-based approach to IoT security –IoT security is critical to all aspects of enterprise and personal security. However, due to the volume of devices and the range of manufacturers who may end up in the same environment, it's next to impossible to ensure the highest level of security in each one.

As cloud-based sensing and actuation along with compiling of data expands, we need to realise the lack of commonality in our understanding. Sometimes it's just the words we use, sometimes it's semantics, and still other times it's our confusion of expected outcomes. In machinery we talk about RPM revolutions per minute or SPM Strokes per minute or spindle speeds or IPM inches per minute, etc. all of these terms are machinery related. Parts per minute, (PPM) is what we really care about. When we look at IoT and our desired outcome, it's the measurement and metrics where we cloud our data and confuse our information.

Many large healthcare organizations are adopting RPA leading to digitalization which can lead to healthy competition between medical services. The use of disruptive science and technology can make the healthcare industry more efficient. In different industries, RPA is considered to be an exploratory step for enterprises and organizations to move into the world of artificial intelligence. According to the recent RPA report, increasing productivity and improving customer experience are the top priorities for organizations to adopt RPA. Currently, healthcare providers and professionals are looking for different ways to promote patient interaction, reduce costs, increase productivity, and increase operational efficiency.

Today, most companies are using Python for AI and Machine Learning. With predictive analytics and pattern recognition becoming more popular than ever, Python development services are a priority for high-scale enterprises and startups. Python developers are in high-demand -- mostly because of what can be achieved with the language. AI programming languages need to be powerful, scalable, and readable. Python code delivers on all three.

Data collaboration is the process of combining datasets together to generate new value from data-driven insights. The datasets being combined can come from different organizations, or they can come from data silos internal to an organization. A number of use cases are possible through data collaboration: fraud detection, advances in healthcare research, real-world data, cross-selling, churn analysis, etc. However, there are significant blockers in realizing the potential benefits of data collaboration. Some of these blockers are so severe that they can stymie potentially valuable collaborations. The blockers originate from a host of areas -- fear of loss of IP (intellectual property), privacy regulations, data residency restrictions, and reputational risk (just to name a few).

In an MIT Sloan Management Review article published last week, David A. Bray and Ray Wang outline the challenges ahead for incorporating people-centered design principles for deep learning. Deep learning, like other types of AI, trains itself, raising questions about accuracy and fairness in the findings. As companies adopt these technologies, "leadership must ensure that artificial neural networks are accurate and precise because poorly tuned networks can affect business decisions and potentially hurt customers, products, and services," Bray and Wang write. They advocate for "a people-centered approach to deep learning ethics," which benefits not just a few individuals, but entire communities. The approach is built on transparency, explainability, and reversibility, they write, which should be the foundation for any AI implementation.

Microsoft CEO Satya Nadella imagines a world with an ever-expanding set of connected devices that process data locally and work in tandem with the cloud – and his company has designed its entire multibillion-dollar cloud business around that concept. Nadella revealed the company's strategy for edge computing during Microsoft's recent shareholders meeting. Edge computing is a buzzword, but it basically means processing data on the devices themselves, instead of offsite in the cloud. Think of a self-driving car. It needs to be able to process data and make split-second decisions without the delays that would come if that data had to be processed far away in the cloud.