The still-ongoing pandemic has pushed many people towards online shopping permanently. And this gave a staggering rise to the ecommerce industry. During the pandemic, there has been a 50% growth in the ecommerce market.
And, as more and more people opt for ecommerce businesses over conventional retail, it is becoming important to integrate artificial intelligence (AI) for efficiency. Taking such a step can prove beneficial for many reasons; driving customer engagement, detecting fraud, retaining customers, and enhancing the shopping experience.
Big players are already deploying AI, and here are five ways you can start leveraging it to grow your ecommerce business.Read more
Over the last decade, there was significant fear that Artificial Intelligence (AI) will replace all humans at the workplace. However, those fears were totally unfounded, and today we work more efficiently and effectively, side by side, with smart algorithms.
According to IDC, enterprises across the planet will spend approximately $656 billion this year on future-of-work technologies. The bulk of that money will go towards AI-powered software for enterprise applications. As most of us will work remotely in the coming years, the industry will also focus more on overcoming challenges related to collaboration and productivity.
Let’s look at how AI is already supporting us at work, transforming how we get things done.Read more
With the rapid adoption of intelligent technology, marketers now have access to a wealth of data-driven insights that were previously unimaginable. These platforms help them to better understand their target audiences while also relieving the workload on team members so they can focus more time on converting customers into raving fans! It’s hard to imagine a company function that could benefit more from artificial intelligence than marketing (AI Marketing).
Its core activities include understanding customer needs, matching those up with products and services available on the marketplace, and persuading people to buy what you’re selling.Read more
Smart algorithms are only as good as their training data sets. As such, it’s not surprising that algorithmic bias (or Bias in Artificial Intelligence = AI Bias) increasingly pops up when Artificial Intelligence (AI) and Machine Learning (ML) models go into production.
AI bias is dangerous because it could easily lead to poor decisions with disastrous consequences. I’m sure you have come across examples of AI bias in the news, like AI’s inability to recognize minorities and so on. So, it’s not hard to imagine businesses finding themselves in a legal nightmare.
How do you overcome or prevent AI bias?Read more
Artificial Intelligence (AI) and its subset Machine Learning (ML) are at the heart of innovation for digitally transformed businesses. However, ML, in particular, needs to be highly interoperable for smart technologies to be truly disruptive and innovative at scale.
If there was no interoperability, you could bet that AI development would be limited and only accessible to big tech. This is because only tech giants have access to the necessary resources and, more importantly, the most data that makes continuous and meaningful learning possible.Read more
The search engine giant Google has always been focused on understanding and responding to its users’ needs. Over the past few years, we have witnessed countless changes in Google’s algorithm, and it’s always been something important to industries.
However, while most of Google’s algorithms changes weren’t earth-shattering, this current evolution just might be it. The introduction of its latest Artificial Intelligence (AI) algorithm called MUM is something we haven’t seen before.Read more
Whenever we discuss the key benefits of artificial intelligence (AI), we think of its application in connected cars, FinTech, and healthcare. While we first encountered smart algorithms in the form of Amazon product recommendations and personal assistants like Siri, this technology has evolved to become so much more.
Some use cases in healthcare and software development were groundbreaking (to say the least). However, every now and then, we come across some surprising applications for new technologies.
Let’s take a look at seven unusual real-world use cases for AI.Read more
According to IDC, as much as 65% of organizations have accelerated the use of digital technologies this year. In this case, technologies like AI will transform existing business processes to boost employee productivity, drive customer engagement, and enhance business resiliency.Read more
From robots on the factory floor to decision-making in investment banks, technology has always driven the financial service sectors. John McCarthy first coined the term artificial intelligence in 1956, but for many this concept from the world of science fiction is only becoming a reality today.
The potential of this technology has driven billions of dollars into research and development around the world; however, there are no clear examples or benchmarks that show us exactly where we may end up regarding making machines think like humans.
Artificial intelligence (AI) is a crucial tool in the financial sector. AI covers everything from chatbot assistants to new systems and tools designed to quickly detect fraud. In addition, AI tools can be used to improve task automation in the financial industry, helping to increase efficiency. While AI may provide a lot of obvious advantages, it’s important to recognize that even now, a significant amount of a bank’s manual procedures are still being done manually.Read more
Recent advancements in artificial intelligence (AI) like autonomous systems, computer vision, natural language processing (NLP), and predictive analytics are all powered by machine learning (ML). In those scenarios, ML helps to move data in the value chain from the informational level to the knowledge level.
Most smart systems you’ve interacted with today were probably developed leveraging supervised learning. Supervised learning is all about building ML models from scratch. However, this approach isn’t always the best. Many AI and ML projects fail because of a lack of resources and, of course, a lack of useful AI training datasets.
Supervised learning demands time, money, and significant human effort to make it work. That’s why it’s vital for enterprises to find viable alternatives to supervised learning. While for many years there has been no way around this problem, ML engineers have recently identified new ways to optimize ML models.Read more