AI News, The Artificial Intelligence Revolution: Part 1 artificial intelligence

The AI revolution – part 1 | VentureWeb

AI technology is already impacting our lives with the use of smart cars, video gaming, virtual assistants, news creation, surveillance, smart homes, wearable technology and more.

On a personal level people have already assimilated with AI on a day-to-day basis from smartphones, smart cars, banking, smart home devices and wearables.

A key example is Microsoft Cortana that is able to go through vast amounts of data to organise, track and analyse its user’s preferences and tailor results to meet the user’s specific request.

It could be asking what meetings you have scheduled that day or where the next Starbucks coffee shop is. Your virtual assistant will find the information you asked for and deliver it via your phone, apps, in-car systems etc.

We can see why Chief Marketing Officers (CMOs) have one of the most challenging jobs in the corporate world. Russell Reynolds did a study that indicated CMOs and their teams face intense pressure in this fast-paced digital era based on technological disruption.

Rising from humble beginnings as a Seattle-based internet bookstore, Amazon has grown into a propulsive force in at least five different giant industries: retail, logistics, consumer technology, cloud computing, and most recently, media and entertainment.” CB Insights AI-powered machines can create targeted content – Using language processing, AI can analyse user interests and geography to create copy that is relevant to the target audience.

It is remarkable to see content generated by AI machines precisely for its users based on psychographics. In addition to creating content at scale, AI is helping streamline the content creation process and also reducing costs.

AI and search engines – Many marketers can agree search algorithms can be tricky to manage with targeting the right keyword phrases, titles and alt tags to rank at the top of search results.

For example, programmatic ads will be more likely to achieve user engagement and clicks since the content will match user preferences based on demographics. AI also makes optimisation of ads more efficient based on algorithms that can help achieve the best cost per acquisition (CPA) from the available data.

Here are some AR examples: Lowe’s Holoroom, IKEA’s Augmented Reality Catalog, Lego X There are many more areas in which AI is impacting businesses and marketing, such as language recognition, website design, sales forecasting, customer service chatbots, product pricing, preventing fraud, data security and more.

Deep Learning and the Artificial Intelligence Revolution: Part 3

Deep learning is a subset of machine learning that has attracted worldwide attention for its recent success solving particularly hard and large-scale problems in areas such as speech recognition, natural language processing, and image classification.

Figure 1: The Neuron Model At a simplistic level, a neuron in a neural network is a unit that receives a number of inputs (xi), performs a computation on the inputs, and then sends the output to other nodes or neurons in the network.

Image classification is an area where deep learning can achieve high performance on very hard visual recognition tasks - even exceeding human performance in certain areas.

Gradient descent emerged as an efficient mathematical optimization that works effectively with a large number of dimensions (or features) without having to perform brute force dimensionality analysis.

Based on the error, gradient descent will then modify the weights, backpropagate the updated weights through the multiple layers, and retrain the model such that the cost function moves towards the global minimum.

The ability to collect and store large volumes of structured and unstructured data has provided deep learning with the raw material needed to improve predictions.

For example, it is not uncommon for frequent modifications to occur during the experimentation process - tuning hyperparameters, adding unstructured input data, modifying the results output - as new information and insights are uncovered.

Therefore, it is important to choose a database that is built on a flexible data model, avoiding the need to perform costly schema migrations whenever data structures need to change.

Deep learning models can take weeks to train - as algorithms such as gradient descent may require many iterations of matrix operations involving billions of parameters.

In order to reduce training times, deep learning frameworks try to parallelize the training workload across fleets of distributed commodity servers.

In addition to the model's training phase, another big challenge of deep learning is that the input datasets are continuously growing, which increases the number of parameters to train against.

Not only does this mean that the input dataset may exceed available server memory, but it also means that the matrices involved in gradient descent can exceed the node's memory as well.

Thus, scaling out, rather than scaling up, is important as this enables the workload and associated dataset to be distributed across multiple nodes, allowing computations to be performed in parallel.

With strong consistency each node in a distributed database cluster is operating on the latest copy of the data, though some algorithms, such as Stochastic Gradient Descent (SGD), can tolerate a certain degree of inconsistency.

The Ai Playbook — part 1: Main trends in artificial intelligence

particularly useful application of Natural Language Processing algorithms is Automated Sentiment Analysis: This technique comes particularly handy when dealing with large datasets or dynamic social feeds like Facebook Newsfeed, as shown below: For an introduction to Sentiment Analysis, we recommend readying The Beginners Guide To Sentiment Analysis, from Matt Kiser: While for a detailed look at a real life application of Natural Language Processing, we recommend this article from our CEO Francesco Stasi: Speech recognition technologies enables machines to respond correctly and reliably to human voices, and provide useful and valuable services based on requests expressed by the users using solely their voice.

If you have developed or planning to develop a chatbot, and you want to know how to make it “smart”, we suggest you ready this step-by-step guide from our co-chief ai scientist Kumar Shridhar: If you are interested in the technical aspects chatbots, and you want to know how a smart chatbot comes to life, we highly recommend this article: This sub field of Artificial Intelligence research is the science of programming software to be able to write like a normal human being and eventually be able to pass the Turing Test.

If you are curious about the Turing Test and how far we are from being able to talk to machines the same way we talk to our friends, we recommend reading this spot on article: While if you want to dive deep into the topic and read about how a chatbot powered by a generative model Ai algorithm is created, we highly recommend reading another article Kumar Shridhar has written on the topic: In the following weeks we’ll be releasing more articles covering more aspect of Ai, its application and the impact it can have on your business.