Articles

AI and Its Discriminating Algorithms

, ,

Artificial Intelligence has taken the industry by storm. It is spreading gems of highly advanced technological traces and with its simple touch, transforming the face of the tech world. As it paves its way into capturing diverse industries, it influences the latest trends and stirring complexities that eventually puts immense pressure on marketers, developers, and creative artists.

However, due to some shocking updates about AI algorithms that have surfaced the industry, many conflicting opinions and judgments spurred among the tech giants. The algorithms of Artificial Intelligence are reportedly being noticed to create racist and biased discrimination.


Source de l’article sur DZONE (AI)

/0 Commentaires/par

How AI Is Challenging Traditional Translators

, ,

In the last decade, translation services have grown exponentially to include hardware devices such as Travis Translator, earphones such as Waverly Labs’ pilot, Microsoft Translator, — which not only translates text, but also speech, images, and street signs — Google translate, and Facebook translation. Translations are occurring faster and with greater accuracy thanks to machine translation. 

But what does this mean for the traditional translator? As an expatriate in Germany, I am a user of both translation services and translation software, so I was interested to find out more. I spoke with the CEO and founder of Gengo, Matt Romaine. He co-founded Gengo in 2009 with the aim to democratize access to the opportunity for language enthusiasts around the world and become the bridge to mass global communication. Gengo offers a crowd-sourced human translation platform now with over 20,000 translators supporting 35+ languages. Their clients include Trip Advisor, Etsy, Salesforce, eBay, Facebook, and Google.


Source de l’article sur DZONE (AI)

/0 Commentaires/par

Top 5 Courses to Learn R Programming

, ,

More and more programmers are learning R programming language to become a Data Scientist, one of the hottest and high paying technical jobs on the planet.

Even though, I am from the Python camp, when it comes on choosing between Python and R for Data Science, Machine Learning, and Artificial Intelligence, mainly because of the awesome libraries like TensorFlow Python offers, I had tried R for a short time.


Source de l’article sur DZONE (AI)

/0 Commentaires/par

Machine Learning for Cybersecurity 101

, ,

The considerable number of articles cover Machine Learning for cybersecurity and the ability to protect us from cyber attacks. Still, it’s important to scrutinize how actually Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) can help in cybersecurity right now and what this hype is all about.

First of all, I have to disappoint you. Unfortunately, Machine Learning will never be a silver bullet for cybersecurity compared to image recognition or natural language processing, two areas where Machine Learning is thriving. There will always be a man trying to find weaknesses in systems or ML algorithms and to bypass security mechanisms. What’s worse, now hackers are able to use Machine Learning to carry out all their nefarious endeavors.


Source de l’article sur DZONE (AI)

/0 Commentaires/par

AdaBoost Algorithm For Machine Learning

, ,

What Is AdaBoost?

First of all, AdaBoost is short for Adaptive Boosting. Basically, Ada Boosting was the first really successful boosting algorithm developed for binary classification. Also, it is the best starting point for understanding boosting. Moreover, modern boosting methods build on AdaBoost, most notably stochastic gradient boosting machines.

Generally, AdaBoost is used with short decision trees. Further, the first tree is created, the performance of the tree on each training instance is used. Also, we use it to weight how much attention the next tree. Thus, it is created should pay attention to each training instance. Hence, training data that is hard to predict is given more weight. Although, whereas easy to predict instances are given less weight. 


Source de l’article sur DZONE (AI)

/0 Commentaires/par

How Good Is Machine Translation?

, ,

One of the lesser known, yet very cool features of Google Docs is its ability to provide a pretty decent translation of any text you enter into it. The functionality highlights the progress that has been made in machine translation in recent years. Indeed, work earlier this year suggested that machine translation is now on a par with humans.

Whilst this is certainly very cool and those results garnered a lot of publicity, it shouldn’t be taken to mean that human translators are heading for the scrap heap just yet. A recent study published by the University of Zurich highlights some of the reasons why.


Source de l’article sur DZONE (AI)

/0 Commentaires/par

“Future” Tech That’s Hiding in Plain Sight: AI

, ,

As hot as stories about Artificial Intelligence (AI), augmented/virtual reality (AR/VR), blockchain, and the Internet of Things (IoT) have been in recent months, we often can’t help but think of these technologies as a long way off from mainstream adoption.

Movies like Ready Player One and Avengers: Infinity War only perpetuate this perception by mixing real technologies with fantasy, making the tech we wield in the real world seem primitive in the process.


Source de l’article sur DZONE (AI)

/0 Commentaires/par

It’s Easier Being Green: IoT and Clean Energy

,

Last week, I attended the WIRED Smarter conference in London and wrote an article about a presentation from the Energy track that explained how Google is using DeepMind to reduce energy consumption in its data centers.

My favorite presentation of the day came from Maria McKavanagh, the Chief operating officer at Verv, called “Creating a new energy marketplace that’s powered by data,” and brought together machine learning, IoT and blockchain.

Source de l’article sur DZONE

/0 Commentaires/par

5 Minute ML: Chatbot (QnA) Demystified

, ,

From a Natural Language Processing perspective, a chatbot normally consists of many parts — small talk, QnA (question and answer — included intent prediction and entity extraction), context handling (user, session etc.), question completion, personalization, sentiment analysis, and so on. Not every chatbot needs all the above-mentioned capabilities. You can have just small talk and QnA and create a chatbot or assemble small talk, QnA and personalization and handle most of the user’s queries. But every chatbot needs QnA.

In this article, we focus on only the Q part of QnA. This is the most complex part of any chatbot framework and needs expertise in Machine Learning, Natural Language Processing and in some cases Deep Learning. Intent Prediction and Entity Extraction are 2 major components of the Q part, which helps the system understand the user query in terms of the answer repository. Answer repository is the domain for which we have built the chatbot. The answer repository can be as simple as a set of FAQs or an excel file or as complex as a database, a SAP system, or a knowledge base.


Source de l’article sur DZONE (AI)

/0 Commentaires/par

Build a Fully Functioning App Leveraging Machine Learning with TensorFlow.js

, ,

TensorFlow.js is a JavaScript library for training and deploying Machine Learning (ML) models in the browser (client-side) and on Node.js (server-side). In this article, I want to describe my experience in building an App (that I recently published on Google Play Store) with this javascript ML library.

However, instead of just going straight into how I built this app using TensorFlow.js, I want to first describe the conditions/needs that led to this choice. I also want to touch a little bit on some other approaches that I realized could be possible that you can take to build an App leveraging machine learning. Finally, I want to leave behind some lessons I learned through this experience and would love your thoughts if you have been experimenting with ML in Apps.


Source de l’article sur DZONE (AI)

/0 Commentaires/par