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
In traditional software development, the code is the most critical part. In contrast, what’s crucial in artificial intelligence (AI) and machine learning (ML) development is the data. This is because AI training data models include multi-stage activities that smart algorithms must learn in order to successfully perform tasks .
In this scenario, a small mistake you make during training today can cause your data model to malfunction. This can also have disastrous consequences—for example, poor decisions in the healthcare sector, finance, and of course, self-driving cars.
So, what training data errors should we look out for, and what steps can you take to avoid them? Let’s look at the top five data errors and how we can prevent them.Read more
Emotion recognition or emotion detection is a method of detecting sentiments based on images, videos, audio, and text leveraging artificial intelligence (AI). In this scenario, emotion recognition technology can use data from different sources like photographs, audio recordings, videos, real-time conversations, and documentation for sentiment analysis.
In recent years, emotion recognition has become increasingly popular. In fact, the global emotion detection market is forecasted to grow to $37.1 billion by 2026.
Part of the “affective computing” family of technologies, the primary objective is to help computers or machines interpret human emotions and affective states by examining non-verbal forms of communication like facial expressions, sentence constructions, the use of language, and more.Read more
Artificial Intelligence is becoming more and more prominent in our everyday life. From Google Assistant to Apple’s Siri, we can interact with computers, smartphones, and other devices as if they were human beings.
However, while a computer can answer and respond to simple questions, recent innovations also let them learn and understand human emotions.One of the latest uses of Artificial intelligence is sentiment analysis using natural language processing (NLP).
Artificial Intelligence (AI) is becoming an ever more important part of our lives. Whether it is in our homes with smart speakers and automation or in the business world, its impact in our lives cannot be dismissed.
However, while the benefits of AI are obvious, in the past, using the technology with language translation was difficult, if not impossible. Language translation is an area that has always required human intervention. There’s simply too much nuance in language for a machine to understand without a lot of training, most often done painstakingly by hand.
In recent years, that situation has started to change. With new advances in Machine Learning (ML) along with the development of neural networks, this once-difficult task is now much more possible.Read more
In recent years, object detection and segmentation have accelerated significantly. Today, smart algorithms can find and classify countless individual objects within a video or an image. Although it was incredibly difficult for machines to do, it’s now part of our daily existence.
Both object detection and segmentation are powered by Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL). In this scenario, convolutional neural networks can locate and identify the class each item belongs to within an image.
It has also evolved to be much more than an intelligent algorithm that can recognize objects in photographs stored in a database. It can now find and classify objects in real-time to enable things like self-driving autonomous vehicles and more.Read more