Tips, Expertise, Articles and Advice from the Pro's for Your Website or Blog to Succeed
Back in the days when I was a junior dev, I used to marvel at my supervising senior dev’s ability to create code at insanely break-neck speed.
Within moments, he’d have a working piece of code with prototyped data almost ready and in a semi-working state. There were a lot of tabs pressed, his fingers never leaving the keyboard to touch the mouse for the duration of his demonstration.
When it comes to winning the race for speed and efficiency, manual document processing is still the Achilles' heel of many organizations. You may have the most sophisticated automatic business process in place, but if you still need human intervention to validate your documents, you’ll always lag behind.
It’s just like the story of the rabbit and the turtle. The rabbit was in good shape to win the race but decided it was a good time to take a nap, allowing the turtle to surpass him. Now think of processes: to submit an expense and get a refund, for example, most steps are automatic, but the validation is still done with human intervention, which will slow the whole process down--that’s the rabbit nap in the story. I know, mind-blowing.
Throughout 2020, the COVID-19 pandemic has dominated headlines around the world, and rightly so. And for the most part, discussions about how it intersected with the technology world have centered on things like contact tracing and epidemiology research topics. But as the pandemic has continued to batter populations and economies everywhere, there's growing evidence that it's becoming a major digital change driver across a wide variety of industries, too.
One after another, surveys continue to uncover an acceleration of the ongoing trend toward AI adoption in multiple fields. One, in particular, found that 68% of businesses have increased their spending on AI technology as a part of their pandemic response. When all is said and done, we could be witnessing a fundamental shift that will leave the economic landscape looking very different than it did less than a year ago.
I’m sure that since you are here, you must have interacted with several chatbots on social media, websites, and applications by now. With chatbots evolving each day, we often struggle in identifying what tools we need and how we should look to manage the same.
In the last few years, we have seen the growing confusion between chatbot development and publishing platforms. So, before we share the best platforms for chatbot development, let us first explain the basic difference between” Chatbot Development” and “Chatbot Publishing.”
In this post, you will learn about how to setup/install MLFlow right from your Jupyter Notebook and get started tracking your machine learning projects. This would prove to be very helpful if you are running an enterprise-wide AI practice where you have a bunch of data scientists working on different ML projects. MLFlow will help you track the score of different experiments related to different ML projects.
In order to install/set up MLFlow and do a quick POC, you could get started right from within your Jupyter notebook. Here are the commands to get set up. MLFlow could be installed with the simple command: pip install mlflow. Within Jupyter notebook, this is what you would do:
In recent years we are seeing a great boom in artificial intelligence. Due to the boom in deep learning, we now have more robust and more general predictive models, being able to face problems that were previously unthinkable.
Self-driving cars, virtual assistants, or new text translators are just some of the examples of the applications that artificial intelligence has today. However, artificial intelligence can be applied to solve practically any problem that arises.
There are several machine learning algorithms that can provide the desired outputs by processing the input data. One of the widely used algorithms is linear regression.
Linear regression is a type of supervised learning algorithm where the output is in a continuous range and isn’t classified into categories. Through a linear regression machine learning algorithm, we can predict values with a constant slope.
While I was referring to the following articles to learn the basics, I realized that I could add a few more things so that the community and developers like me can benefit
https://www.analyticsvidhya.com/blog/2017/12/introduction-to-recurrent-neural-networks/
If you want your chatbot to be able to know the answer to more than just the things you teach it about your business you can integrate to other services. Why invent the wheel twice? With integrations, you can have loads of information that might change from day to day without having to constantly update your solution manually.
Let’s look into how you add an integration in Teneo Studio.
In this article, I will talk about AutoML, one of the features that come with the Oracle Cloud Data Science Service, and I hope it will be a useful article in terms of awareness.
As it is known and mentioned in my previous articles, Oracle recently added a new service called Data Science to cloud services. This service has been offered to users as a platform where many libraries come pre-installed. This platform, which includes many features like prototype development, project development, model management, to the production of produced models, contains many new features. Undoubtedly, one of the most interesting and useful features is the AutoML feature.
Artificial intelligence (AI) is shaping up to be the most powerful and transformative technology to sweep the globe and touch all facets of life — economics, healthcare, finance, industry, socio-cultural interactions, etc. — in an unprecedented manner. This is even more important with developments in transfer learning and machine learning capabilities.
You may also like: Transfer Learning: How to Classify Images Using TensorFlow Machine Learning
Already, we are using AI technologies on a daily basis, and it is impacting our lives and choices whether we consciously know it or not. From our Google search and Navigation, Netflix movie recommendations, Amazon purchase suggestions, voice assistants for daily tasks like Siri or Alexa, Facebook community building, medical diagnoses, credit score calculations, and mortgage decision making, etc., AI is only going to grow in adoption.
Messaging services set the stage for humans to interact with programmable robots using the same devices we already use to talk with each other. That kind of interaction feels a little like magic, but it’s magic that anyone who codes can conjure. To show you what I mean, we need to look at Misty’s Photo Booth skill, which Misty demoed at Twilio SIGNAL 2019.
When this skill runs, you can send an SMS to ask your Misty robot to take your picture. When Misty gets your text, she stops what she’s doing, turns to look at you, and snaps your portrait with the camera in her visor. She then sends that picture right back to your phone via MMS.
With the knowledge graph space exploding on all accounts (interest, use cases, funding), centrifugal and centripetal forces are simultaneously at play. While the "wild, early days" of knowledge graph technology are gone, the 20 year anniversary of the Semantic Web is a good opportunity to reflect on what worked and what didn't and to move forward in a pragmatic way.
A testament to the fact that this space is booming: more offerings are available every day, the quality and quantity of knowledge sharing is rising to meet the demand, and at the same time we are starting to see consolidation — in vendors, models, and standards.
If you’re a self-professed (or otherwise acknowledged) robotics geek (like me), you don’t want to miss this Kickstarter project from Sphero – you know, the company that brought you the take-home version of Star Wars’ BB-8.
Launched just this morning and already up to more than 500 backers for a grand total of almost $135,000 pledged, the Kickstarter project hopes to make the company’s newest robot, RVR, a reality. (I’ve actually had to update those numbers twice, strike that, three times while attempting to post this article.)