How AI Startups Are Transforming the Future of Work!

 In the coming years, AI startups are poised to revolutionize the way we work. With the advancement of technologies such as machine learning, natural language processing, and computer vision, these startups are developing innovative solutions that will transform the future of work. Here are some ways AI startups are shaping the future of work:

AI Startups Are Transforming

• Automation and Augmentation: AI-powered automation and augmentation tools will enable workers to complete tasks more efficiently and accurately. These tools will help reduce repetitive and mundane tasks, freeing up time for workers to focus on more complex and creative tasks.

• Personalization and Adaptation: AI will enable the creation of personalized learning and development programs for employees. These programs will adapt to individual learning styles and preferences, improving the effectiveness of training and development initiatives.

• Collaboration and Communication: AI-powered collaboration and communication tools will enable remote teams to work together more effectively. These tools will facilitate real-time collaboration, streamline communication, and improve team productivity.

• Talent Acquisition and Retention: AI will enable more accurate and efficient talent acquisition and retention processes. AI-powered recruitment tools will help identify the best candidates for a job, while AI-powered retention tools will help organizations identify at-risk employees and take proactive measures to retain them.

AI startups are likely to contribute to this transformation

• Automation and Efficiency: AI startups are innovating automation technologies that streamline repetitive tasks across industries. This not only reduces mundane work for employees but also enhances overall efficiency and accuracy within workflows. Whether in manufacturing, customer service, or administrative tasks, AI-powered automation has become more sophisticated, enabling businesses to reallocate human resources to higher-value tasks.

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• Enhanced Decision-Making: AI algorithms are evolving to provide more accurate and actionable insights. Startups are developing AI systems capable of analyzing vast amounts of data to assist companies in making informed decisions. These systems offer predictive analytics, helping businesses foresee market trends, customer behaviors, and potential risks.

• Personalized Learning and Development: AI-driven personalized learning platforms are revolutionizing employee training and development. Startups are leveraging AI to create adaptive learning systems that tailor content to individual employees' needs, learning styles, and progress, enhancing skill acquisition and retention.

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• Remote Work Facilitation: The shift to remote work accelerated by the COVID-19 pandemic has led to increased demand for AI tools that facilitate remote collaboration, communication, and project management. AI startups are developing advanced platforms for virtual meetings, team collaboration, and remote project management, optimizing productivity for distributed teams.

• Health and Well-being: AI startups are also focusing on employee health and well-being by developing applications that monitor stress levels, promote mental health, and encourage physical fitness. These tools aim to create a healthier and more engaged workforce.

In conclusion, AI startups are transforming the future of work by automating and augmenting tasks, personalizing learning and development, facilitating collaboration and communication, improving talent acquisition and retention, and enabling more accurate and efficient workforce planning and development. These innovations will enable organizations to be more productive, efficient, and competitive in the years to come.

 Artificial Intelligence: The Future is Data Capture, Not Machine Learning

A 2021 report from KPMG shows artificial intelligence (AI) is progressing much faster than anticipated, with skyrocketing adoption driven partly by the Covid-19 pandemic. Researchers at Oxford University in

England estimate that by 2024, AI will be better than humans at translation, will write bestselling books by 2049 and will perform surgeries by 2053. Machine learning (ML), the proficiency of a machine to mimic human ability to accumulate knowledge and use it to drive insights, is generally considered the basis of AI.

AI’s Dependence on Data

Although AI might depend on its machine learning abilities, we need to take a step back and realize ML doesn’t happen in a vacuum. ML is driven by big data, without which it can’t take place. Effectively, therefore, AI depends completely on the amount of data we can capture and the methods we use to process and manage it. For this reason, I believe we need to pay more attention to data capture, transport, processing, and storage if we want to realize the promise of AI in the future.

The Importance of Data Capture

Capturing data is essential, whether it’s for software-based AI applications, smart robots based on AI, or machine learning. When AI products were initially designed, developers spent huge research and development resources collecting human behavioral data, both on the industry side and the consumer side. In healthcare, many smart applications offer predictive analysis for prognoses and treatments. While these programs are becoming progressively smarter, they could be made even more accurate by applying increased intelligence gathered from human data.

User data is critical for developing technologies with higher intelligence, whether these are software systems, hardware devices, IoT devices, or home automation equipment. However, one of the most difficult aspects of capturing data in edge environments is transmitting it securely to a data center because of the threat of ransomware attacks or viruses.

With Data, More IS More

Projections from Statista indicate that by the end of 2025, the world will potentially generate 181 zettabytes of data, an increase of 129% over 2021’s 79 zettabytes. This applies particularly in medical science, where various organizations collect massive amounts of data.

For example, data from the first Covid-19 vaccines administered helped to determine the accuracy of doses for all age groups. Similarly, we need more data to achieve greater accuracy and more effective devices, whether for software, robotics, or anything else.

We also need more data from real edges, whether these are static or moving, and regardless of how remote their location, to be able to run timely AI and ML applications.

The future of AI will depend on capturing more data through real-time applications from edges such as a gas pipeline, a submarine in the ocean, a defense front, healthcare, IoT devices, satellites, or rockets in space.

The Challenges of Managing Data

To optimize AI for the future, we also need high-performance systems. These could be storage or cloud-based systems, processed by modern, data-hungry applications. The more data you feed these applications, the faster they can run their algorithms and deliver insights, whether these are for micro strategy tools or business intelligence tools. This is usually called data mining, and, in the past, we did it by putting the data into a warehouse and then running applications to process it.

However, these methods are rife with challenges. Data-generating devices are now continuously churning out ever-growing amounts of information. Whether the source is autonomous vehicles or healthcare, and whether the platform is a drone or edge device, everything is capable of generating larger amounts of data than before. Until now, the data management industry has not been able to capture these quantities, either through networks, 5G, cloud, or any other storage method.

These circumstances have led to 90% of data gathered being dropped because of inadequate storage capacity and the inability to process it quickly and deliver it to a data center. The outcomes also apply to critical data captured at remote sites that have no connectivity or cloud applications running at the edge.

Forward to the Future

The more data we have, the better AI performs. The more information we can gather in real-time from real users on the ground, the smarter we can make our AI devices. The more we can make AI applicable to the use cases, the more human we can make the connection, and the better we can solve the users' problems. To date, much of the big data we generate goes unused, primarily because organizations cannot capture, transport, and analyze it fast enough to create real-time insights. It’s essential for us to develop ways to resolve these challenges, to enable us to enjoy the advantages of putting AI to work for humanity.

Robotics Systems to Get a Push in Service Industry with a New Partnership

In a new announcement, SoftBank Robotics Group Corp (SBRG) and Keenon Robotics have announced a partnership to help expand the use of robotics systems in the service industry for better efficiency and productivity.

Based out of China, Keenon Robotics is a global leader in indoor intelligent service robots. The company will work in conjunction with SoftBank Robotics Group to increase the efficiency by bringing about the application of robotics systems. This will allow businesses to allocate more time and resources for service crews to refocus on customer service.

"At SoftBank Robotics, it is all about leveraging the technologies of tomorrow to create better solutions," said SoftBank Robotics Corp., Kenichi Yoshida, Chief Business Officer. "The partnership with Keenon Robotics, the global leading AI-company focusing on indoor intelligent service robots will allow us to deliver cutting edge solutions as an integrated system to increase capability and efficiency in the service industry as well as achieve greater savings."

This partnership is largely being seen as a follow-up from the SoftBank World event announcement on 15^th September 2021, which declared the collaboration between SoftBank Robotics and Keenon Robotics.

 

 

IronNet Makes its Collective Defense Solution Available To All Singapore Enterprises

IronNet has announced that it is making its flagship Collective Defense solution available to all enterprises in Singapore. The technology will empower organizations in Singapore to stay ahead of the evolving threats and defend their network through real-time cyberattack intelligence sharing and collaboration across industries and sectors.

“It is very exciting to see IronNet's Collective Defense platform expanding globally into Singapore. Our approach is truly a revolutionary one, enabling AI-based threat detection and collaboration across enterprises for a stronger, more proactive cyber defense,” said General (Ret.) Keith Alexander, Founder and Co-CEO at IronNet Cybersecurity.

The Collective Defense Solution platform uses behavioral analytics and artificial intelligence to deliver visibility across enterprise ecosystems, group-level detection and correlation, and instant collaboration with fellow defenders.

“Given the shortage of skilled cybersecurity operations resources coupled with rapidly evolving cyber threats, it is vital to build a cyber-defense framework that can leverage our collective wisdom across enterprises and help scale over time. This expansion of the Singapore Collective Defense community allows businesses in Singapore to detect threats together and defend together,” said Gaurav Chhiber, VP of Asia Pacific and Japan at IronNet.

 

Black & Veatch is joining hands with Big Blue to work on AI-driven solutions

Black & Veatch, an engineering and construction company from Kansas, has recently made an announcement that it is partnering with the Big Blue IBM to market APM solutions. This also includes remote monitoring techs that make use of real-time data analytics with AI to help consumers maintain their assets and equipment. By using the marketed solutions, customers can keep their assets at peak performance and reliability. The main motto of the recent collaboration is to combine IBM’s Maximo App Suite with Black & Veatch’s AMS services. These solutions are expected to help organizations support more demanding operations for energy, utilities, and industrial assets.

 Black & Veatch is well-known for its four diagnostics and monitoring centers used for real-time analysis and detection of upcoming issues by running many scenarios and models to accurately predict the changes in asset performance. IBM application’s Monitor, Predict, Assist, Visual, and Health inspection capabilities will combine with Black & Veatch’s diagnostics and monitoring capabilities to bring them on to a field where the gathered insights can be applied. Dave Brill, Black & Veatch’s VP of Asset Management Services, has stated that digital twins are an integral part of the industrial segment’s digital future. This is mainly because they can provide a brief understanding of complex assets.

 

Google Unveils its New Tensor Chipset to Power Pixel 6 phones

 

In a recent press meet, Google has announced that it has made its own SoC to power the AI and machine learning (ML) process for its upcoming Pixel 6 series phone. The all-new Google Tensor chipset will be used in both Pixel 6 and Pixel 6 Pro phones. The tech giant has been working for over five years to build the Tensor chipset and the company's CEO, Sundar Pichai. This new chipset will increase Google's computing abilities.

Google has made this chip to process the necessary AI features for videos and photos efficiently. It will enable the company to bring innovative AI and ML technologies to its end users. Besides, Google is aiming to provide a more personalized experience with its Pixel phone series so that every Pixel device feels unique. Be it the AI-enabled solutions, features, suggestions, color themes, photography, or voice commands.

With the new Tensor chipset backed by robust and innovative AI and ML capabilities, Google aims to provide a truly personalized smartphone. The new chipset not just contains computing resources. It will also include the latest technologies based on AI-based algorithms to unlock the specific experiences for all the Pixel users.

Technology to Rely on Artificial Intelligence (AI) in the Future

By 2023, over 40 per cent of technology will rely on Artificial Intelligence (AI) by 2023 which is a 5 per cent increase from today. “Privacy laws, such as General Data Protection Regulation (GDPR), presented a compelling business case for privacy compliance and inspired many other jurisdictions worldwide to follow,” said Bart Willemsen, research vice president at Gartner.“More than 60 jurisdictions around the world have proposed or are drafting postmodern privacy and data protection laws as a result. Canada, for example, is looking to modernize their Personal Information Protection and Electronic Documents Act (PIPEDA), in part to maintain the adequacy standing with the EU post-GDPR.”

Leaders are under pressure to make sure that all personnel data processed is brought in scope and under control which is a difficult and expensive task to manage without the aid of technology. This is where the use of AI powered applications comes in to reduce administrative burdens and manual workloads. In Gartner’s 2019 Security and Risk Survey, many organizations are incapable of delivering precise answers to the SRRs they receive. Often a manual job, the average costs of these workflows are roughly $1400 dollars that accumulate over time.

“The speed and consistency by which AI-powered tools can help address large volumes of SRRs not only saves an organization excessive spend, but also repairs customer trust,” said Mr. Willemsen. “With the loss of customers serving as privacy leaders’ second highest concern, such tools will ensure that their privacy demands are met.”

Planning to become IoT developer, then you should learn about these difficulties and know what you are going to deal with in the process

A relatively new area of IoT development is only gaining its popularity and relevance. People connect cars, portable devices at home, creating an entire ecosystem of data that developers need to work with. The pioneers of this development have already overcome some difficulties, some just discovered and are working on them. Before becoming an IoT developer, you should learn about these difficulties and know what you are going to deal with in the process.

What Does Await for IoT in the future?

The Internet of Things is a new stage in the development of the Internet when more things are connected to it than people. IoT connects the objects around us to a computer network. They exchange information with each other and work without human intervention and in real-time. In fact, it is the Internet, a new real world.

The Internet of Things promises to change the way whole industries approach their business. This innovation requires the business to both fully digitalize internal processes and change business models for better UX. IoT users are already becoming more and more all over the world, from China to the USA and analysts' forecasts call these processes only the beginning of the development of new technology. The concept itself takes the first place among the predictions of futurologists, but its peculiarity is that it has already begun to be realized.

To hire IoT application developer, you need the following points within the company:

Connected Products: A company can develop new IoT devices and services and combine them into one ecosystem (for example, Apple does this by creating iCloud to upload all the information from user devices there, thus linking them.)

Connected Business Processes: A company can use IoT to improve its business processes.

What are the IoT Applications in different industries? We can already name several areas that use IoT Applications in their business processes.

Smart Home

More and more companies are becoming involved in the creation of smart homes, as demand increases among consumers. Major market players such as Philips and Belkin are already actively occupying a niche, but the area still waits for new startups for further development.

Smart City

The explanation of the meaning is in the concept itself. The concept of a smart city consists of water distribution, waste management, traffic management, and environmental monitoring. Such industrial IoT will improve the quality of citizens’ life, get rid of old problems and make cities safer for life.

Wearables

Devices that can be interconnected are the most popular for potential buyers.

Connected Car

This application requires powerful networking, so this technology is closely related to the deployment of 5G technology.

Connected Health

Already, the development of new remote monitoring equipment has great potential and will certainly have its customers. The healthcare area in particular needs IoT applications.

Smart Farming

Most farming processes can be monitored using IoT, which would reduce the time and other resources spent on growing crops.

In the future, the business will have an even more serious approach to IoT. Predictive maintenance will be improved by several more levels, which will entail an increase in technology investment. Other predictions also include gaining voice by all IoT devices. Virtual assistants will become part of the smart device itself, as voice control will improve the technology. We must not lose sight of the impact of AI on the development and implementation of IoT technology. Internet of Things creates a huge amount of information. Analysis and outgoing data will be difficult to regulate by a human, therefore only the combination of these two technologies will raise them to the next level of development in the future.

5 pitfalls of IoT Developers that You Can Learn From

As the Internet of Things grows, so does the demand for programmers. This suggests what opportunities will open for them in the coming years. Let's talk about what you need to know before deciding to become an IoT developer.

Basic knowledge of languages

The Internet of Things is so widespread that a developer can choose a programming language to his liking. Low-level assembly or C / C ++ programming is needed for embedded systems.

High-level languages ​​such as Node.js and Java will be a great start for beginners in IoT programming. They have a low entry threshold and also, have useful features, such as automatic memory deallocation. It is also important to be able to understand the SoC directory and understand how the sensors interact with the DAC.

Security

This problem is raised every time it comes to IoT. With so many connected devices, it can be difficult for users to protect their personal data and application patterns. The more devices are connected, the more vulnerabilities and IoT security threats. Among them are attacks on cloud services that store large and often confidential amounts of data. This makes them an attractive target for hackers who are very inventive in their search for new attack vectors for centralized services. In the event of hacking of a cloud provider alone, the damage can range from 50 to 120 billion dollars.

Data validity

Data stored on IoT platforms cannot be completely trusted outside the scope of responsibility of one owner since it is not possible to verify that they have not been changed before being sent, sold or used by other parties.

Large companies such as Uber and Lyft do not have a solution that allows them to share reliable map data or travel data; they found another way: to collect and store such data sets independently of each other.

Privacy

Another topical issue of IoT is user privacy. And it's not only about the possible theft of data by hackers, but also about a violation of the privacy of the consumer. Enhanced corporate transparency is required to ensure the invulnerability of user data.

Lack of control system

Another problem is that the IoT industry faces is its insufficient scaling - if forecasts regarding the number of connected devices shortly come true, it is difficult to imagine a functioning network supported by existing inefficient and insecure centralized solutions.

These are the problems that IoT programmers have yet to figure out. Demand for specialists in this field is only growing now, and in particular, demand for those who understand the vulnerabilities of hardware and software used by devices connected to the network. It is on these 5 pitfalls that the best programming minds are now beating.

Conclusion

The Internet of Things is an area that is currently new. How the industry will develop in the future is already predicted by tech evangelists. Security and centralization problems are still open, but with the introduction of 5G and AI, IoT developers will be able to cope with them and thus transfer the technology to a new level of development.