History of Machine Learning – A Journey through the Timeline

Machine learning is a subset of Artificial Intelligence (AI) which uses algorithms to learn from data. If you are familiar with AI as it has been through the news recently in regards to fending off cyber attacks or causing self-driving cars accidents then this will be easy for you understand. The idea that machines can “learn” without being programmed by humans goes back further than just recent years though; think about the first computers in the history of machine learning and how they could only do one thing at a time. For a practical example of this concept in action, consider how Clickworker facilitated the training of face recognition software through their innovative crowd-sourced approach. You can read about this case study here.

All about Human Intelligence & Artificial Intelligence – differences, strengths and weaknesses, human AI and more

Artificial intelligence has been a fascinating topic for decades, but now the technology is becoming more accessible. But does it have anything to offer us? What are the differences between human and artificial intelligence and what does the resulting human AI bring us?
We will answer these questions in this article.

The Value of Data Quality and Data Diversity in AI models

There are many different types and quality dimensions of data that contribute to an artifical intelligence (AI) model. The type and quality matter a lot, but so does the diversity. When it comes to models, accuracy is determined by how much variability there is in your dataset- more diverse means less bias because you have more options on what features exist within your dataset. Understanding the process of AI training can also provide deeper insights into why data diversity plays such a crucial role in the development of unbiased, effective AI systems.

How AI in Traffic Management is Helping to Ease Traffic Congestion

The rapid advancement of artificial intelligence (AI) has brought about a sea change in road traffic management. AI can now predict and control the flow of people, objects, vehicles and goods at different points on the transportation network with great accuracy. In addition to providing better service for citizens than ever before, AI is also making it possible to reduce accidents by optimizing flows across intersections as well as improving safety during periods when roads are shut down due to construction or other events. Furthermore, AI’s ability to process and analyze vast amounts of data has allowed for effective mass transit, such as ride-sharing services. So how is AI revolutionizing road traffic management?

20 Open-Source Machine Learning Datasets

When it comes to machine learning, data is key. Without data, there can be no training of models and no insights gained. Thankfully, there are many sources from which you can obtain free machine learning datasets. To dig deeper into the intricacies of preparing data for machine learning, including the process of AI training, can provide valuable insights. Find the most useful open source datasets, and learn what to look out for before acquiring one.

How to Transcribe Audio to Text: A Guide to Audio Transcription and Speech Recognition

Transcribe audio to text can be a valuable process for creating accurate records of conversations, tracking and transcribing speeches, and more.

In this guide, we will discuss the benefits of audio transcription, its use cases, speech to text, AI transcription and human transcription and speech recognition. By learning about these topics, you’ll have a better understanding of how they work and how you can put them to use in your own business or personal life.

Human in the Loop: The Human in the Machine

Man in the machine – a buzzword familiar from science fiction novels of the early 20th century. What this term is about in the 21st century is clear: it is about Artificial Intelligence and Machine Learning. The development and training of AI requires the intervention of natural intelligence at many points: human in the loop. In this loop, the human acts in a similar way to a teacher.

5 Ways to Integrate AI in Your eCommerce Shop

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.

The Future of Marketing is AI

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.

Top 9 Ways to Overcome or Prevent AI Bias

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. To understand more about how to avoid training data errors that might lead to these biases, consider reading up on the importance of high-quality AI datasets at Clickworker’s guide on avoiding training data errors.

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?