Willie  Beier

Willie Beier


Continuous Intelligence: Keeping your AI Application in Production

It is already challenging to transition a machine learning model or AI system from the research space to production, and maintaining that system alongside ever-changing data is an even greater challenge. In software engineering, Continuous Delivery practices have been developed to ensure that developers can adapt, maintain, and update software and systems cheaply and quickly, enabling release cycles on the scale of hours or days instead of weeks or months. Nevertheless, in the data science world Continuous Delivery is rarely been applied holistically.

This is partly due to different workflows: data scientists regularly work on whole sets of hypotheses, whereas software engineers work more linearly even when evaluating multiple implementation alternatives. Therefore, existing software engineering practices cannot be applied as-is to machine learning projects. Learn how we used our expertise in both fields to adapt practices and tools to allow for Continuous Intelligence–the practice of delivering AI applications continuously.


What is GEEK

Buddha Community

Continuous Intelligence: Keeping your AI Application in Production
Otho  Hagenes

Otho Hagenes


Making Sales More Efficient: Lead Qualification Using AI

If you were to ask any organization today, you would learn that they are all becoming reliant on Artificial Intelligence Solutions and using AI to digitally transform in order to bring their organizations into the new age. AI is no longer a new concept, instead, with the technological advancements that are being made in the realm of AI, it has become a much-needed business facet.

AI has become easier to use and implement than ever before, and every business is applying AI solutions to their processes. Organizations have begun to base their digital transformation strategies around AI and the way in which they conduct their business. One of these business processes that AI has helped transform is lead qualifications.

#ai-solutions-development #artificial-intelligence #future-of-artificial-intellige #ai #ai-applications #ai-trends #future-of-ai #ai-revolution

Obie  Rowe

Obie Rowe


Learning When and When Not to Leverage AI in Your Products

You need to go from your house to the Airport. Do you take a Limo or a bike? Of course a Limo? The road is bad and the traffic worse… A Limo is not always the right choice.

Product Managers solve user problems. Sometimes AI is the answer to all your problems. Other times, it is not worth the trouble.

The question becomes, when and where should we leverage AI in our Products?

My first job as a Product Manager was in an AI based startup whose core competency was image and video based analytics. I was exploring the feasibility and applications in the Security Surveillance space.

What I found surprised me.

One of my visits was to a company helping the Singapore govt with the Surveillance of the country. Singapore has one of the finest infrastructures of the world. And it maintains it beautifully. Littering is a punishable offence. One aspect, hence also becomes ensuring that people don’t throw garbage from the balconies of their highrise buildings.

The few rooms had its walls completely plastered with hundreds of screens. Around 1 person per wall was busily looking at multiple screens at a time trying to detect violations. 24X7 monitoring across thousands of cameras was not an easy task.Was it practical? I would say no, not if done manually.

So here is how they handled it.

They added pixel monitors on each of the balcony railings within range. Any pixel changes flagged the image and people would set forth to manually analyze them.

There were two main problems. First, this was, of course, not scalable. Second, There were too many false positives. Anyone randomly roaming around in their balcony would trigger the alarm. Needless to say, this was very expensive to implement. That was when I was convinced that an AI could do this better and more effectively.

Just like this use case, there are many problems that could be solved by AI.

But what are those problems? When do you even dabble with AI to solve your problems.

It is worth a serious consideration because AI is not without its limitations and challenges. AI done wrong often leads to extremely high costs without the added value. Un-Explainability of results and inconsistent responses are other factors often hampering the reliability.

So, what are some guidelines that will help you decide if to go the AI route.

Do not use AI if:

  • Your problems can be solved by simple rules
  • If you need an explanation of why you received the output that you did. AI is often unexplainable.
  • You need a 100% accuracy 100% times
  • If you do not have good quality and quantity of data
  • If your product includes one or more of the following problems, you could leverage AI

1. Ranking and recommendation

When you visit Amazon app with an intention to buy a product, it is important to Amazon that you make a purchase. With thousands of Products in a single category, how does Amazon shows you the product that you will like? It hence utilizes your behavioral patterns, the characteristic of products, and other parameter to predict the products you are likely to purchase. It can do so without AI as well, but then keeping a track of your changing preferences, purchasing patterns need constant adaptation. AI hence solves this problem beautifully.

#product-management #artificial-intelligence #business #ai #ai-applications #when-to-use-ai #product #hackernoon-top-story

Murray  Beatty

Murray Beatty


This Week in AI | Rubik's Code

Every week we bring to you the best AI research papers, articles and videos that we have found interesting, cool or simply weird that week.

#ai #this week in ai #ai application #ai news #artificaial inteligance #artificial intelligence #artificial neural networks #deep learning #machine learning #this week in ai

This Week in AI - Issue #22 | Rubik's Code

Every week we bring to you the best AI research papers, articles and videos that we have found interesting, cool or simply weird that week.Have fun!

Research Papers


#ai #this week in ai #ai application #ai news #artificaial inteligance #artificial intelligence #artificial neural networks #deep learning #machine learning #this week in ai

Artificial Intelligence: The Future of Modern Life Or a Cruel Deterrence?

Artificial Intelligence is now one of the most important emerging technologies to attempt to imitate human reasoning in AI networks. The benefits of Artificial Intelligent Software are tremendous and will revolutionize almost any industry. This technology is still in its early stages and there are many pros as well as cons of this technology. Although we cannot foresee the future, Artificial Intelligent software has the potential to significantly impact all industries and even society as a whole.
In the future, we are likely to see a vast improvement in AI technology and we will no longer need humans to operate those systems which make machines. We will also no longer need humans to program them. Nevertheless, the main question that remains is whether or not artificial intelligence is “good enough” and whether or not we are ready for that drastic change in our way of life. To date, we still have a long way to go and researchers are continuously making breakthroughs.

#ai #artificial intelligence #artificial intelligence (ai) #artificial intelligence and machine learning #ai algorithm #ai advantage