When "virtual" became the standard medium in early 2020 for business communications from board meetings to office happy hours, companies like Zoom found themselves hot in demand. They also became prime targets for the next big cyberattack. In the case of Zoom, hackers succeeded in April 2020, with a large data breach that exposed an estimated 500,000 user passwords. This breach, along with the thousands of daily reported cyber attacks in that year show that of the major threats facing businesses today, cybercrime nears the top of the list. Due to a combination of factors, including operations that have moved online and sensitive information crossing remote networks, opportunities for cybercrime are rife and the attacks are becoming more advanced and difficult to contain.

Join us on November 9 to learn how to successfully innovate and achieve efficiency by upskilling and scaling citizen developers at the Low-Code/No-Code Summit. With research showing that private investment in artificial intelligence (AI) reached roughly $93.5 billion in 2021, it's no secret that many organizations are implementing AI and machine learning (ML) to improve their businesses, but it's easy to overlook the security risks created by AI adoption. Every AI and ML model that an organization uses can be a potential target for cyberattacks. The good news is that a growing number of providers are recognizing these models as part of the modern enterprise attack surface. One such provider is HiddenLayer, which today announced the launch of the HiddenLayer MLsec Platform designed to detect adversarial ML attacks. The announcement comes hot on the heels of raising $6 million in seed funding earlier this year.

Artificial intelligence is the future, but it already has a prominent standing in the present. As data science gets more sophisticated and consumers continue to demand a more personalized customer experience, AI is the tool that will help enterprises better understand their customers and audiences. But even though AI has all the potential in the world, if we cannot figure out how to address the ethical challenges that remain, this full potential may never be reached. As this technology evolves, one question should remain in the minds of all leaders seeking to implement an AI strategy: How can I ethically and responsibly make the most of AI within my organization? About seven years ago, Gartner released what they referred to as the "Hype Cycle for Emerging Technologies," which highlighted the technologies it predicted would change society and business over the next decade.

Artificial Intelligence and Machine Learning is affecting every area of our lives and society. Google, Amazon, Netflix, Uber, Facebook and many more industries are using AI and ML models in their products. The opportunities and advantages of Machine Learning is quite numerous. What if you could also build your own machine learning models? What if you can build something useful from the ML model you have spend time creating and make some profit whiles helping people and changing the world?

It's critical to keep a finger on the pulse of AI developments. Here are three trends that those involved in creating successful AI and ML models need to heed. Artificial intelligence (AI) and machine learning (ML) models hold the potential to identify customer trends and patterns, to quickly adjust at scale to improve business insights and processes, and to generate new revenue streams. However, the promise of AI and ML models to make things easier by computerizing human cognition has seen its challenges and will surely see more as the industry matures. It's critical to keep a finger on the pulse of AI developments because it helps to learn from others' mistakes as well as their victories.

Artificial Intelligence and Machine Learning is affecting every area of our lives and society. Google, Amazon, Netflix, Uber, Facebook and many more industries are using AI and ML models in their products. The opportunities and advantages of Machine Learning is quite numerous. What if you could also build your own machine learning models? What if you can build something useful from the ML model you have spend time creating and make some profit whiles helping people and changing the world?

In recent years, the banking industry has been at the forefront of AI and ML adoption. According to an Economist Intelligence Unit adoption study, 54% of banks and financial institutions with more than 5,000 employees have adopted AI. But AI and ML adoption has not been easy. Difficulty in deployment has been exacerbated by the growing number of new AI platforms, languages, frameworks, and hybrid compute infrastructure. Add to this the fact that models are being developed by staff in multiple business units and AI teams, making it difficult to ensure that the proper risk and regulatory controls and processes are enforced.

"AI and ML enhance the adoption of IoT by adding the Return on Investment (RoI) layer on top of the IoT stack for both businesses and consumers."- Utility and convenience have been one of the primary forces behind increased adoption of IoT in the Appliances and Consumer Electronics (ACE) sector in India. With Smart IoT powered appliances, end users can control and monitor appliances, set schedules, and automation rules using mobile apps and voice commands from voice assistants such as Google Home, Alexa, and Siri. Acknowledging this market trend, almost all major brands are adding IoT enabled appliances to their catalogue. From smart lighting to smart air conditioners and smart water purifiers, the Indian market has started witnessing the adoption of IoT products.

According to Accenture, 77 percent of smart devices include at least one AI feature. It is anticipated that by 2025 the global AI market will reach $60 billion. The growth of AI has created greater demand for this technology from consumers and organizations alike. Both are embracing AI technology and driving further innovation with their adoption. There is a range of tools for incorporating AI into your workflows and products.

Today, machine learning (ML), artificial intelligence (AI) and decision optimization are not just buzzwords found all over the news. They are urgent requirements for many companies that fear disruption, want to perform pragmatic analysis and make better decisions with their data. Data has been called the next natural resource, like oil. But just as with oil, it must be refined to be valuable, and its end value must exceed the cost of refining it. With data, the value of AI is the cost of investing in collecting, organizing and analyzing all that data.