This ebook, based on the latest ZDNet / TechRepublic special feature, advises CXOs on how to approach AI and ML initiatives, figure out where the data science team fits in, and what algorithms to buy versus build. It depends who you ask. Back in the 1950s, the fathers of the field Minsky and McCarthy, described artificial intelligence as any task performed by a program or a machine that, if a human carried out the same activity, we would say the human had to apply intelligence to accomplish the task. That obviously is a fairly broad definition, which is why you will sometimes see arguments over whether something is truly AI or not. AI systems will typically demonstrate at least some of the following behaviors associated with human intelligence: planning, learning, reasoning, problem solving, knowledge representation, perception, motion, and manipulation and, to a lesser extent, social intelligence and creativity. AI is ubiquitous today, used to recommend what you should buy next online, to understand what you say to virtual assistants such as Amazon's Alexa and Apple's Siri, to recognise who and what is in a photo, to spot spam, or detect credit card fraud. AI might be a hot topic but you'll still need to justify those projects.

The scikit-learnthe library has functions that enable us to build these pipelines by concatenating various modules together. We just need to specify the modules along with the corresponding parameters. It will then build a pipeline using these modules that processes the data and trains the system. The pipeline can include modules that perform various functions like feature selection, preprocessing, random forests, clustering, and so on. In this section, we will see how to build a pipeline to select the top K features from an input data point and then classify them using an Extremely Random Forest classifier.

Since the 1950s, artificial intelligence has been spreading its wings and recently have been in the limelight. Top leaders of the world including Microsoft, Google, Amazon, and Facebook are emphasizing their interest in AI. More than ever AI has been important in our lives today. With excessive production of data, advanced usage of algorithms and improvements made in the field of computing and storage, the enthusiasm for this technology does not cease. Artificial intelligence has impacted the automating businesses in a manner that the growing interest in machine learning, artificial intelligence, and deep learning has seen leaps and bounds and is getting immense popularity in the IT industry these days.

We are going to need the IoT (Internet of Things) to help solve the problems that are facing agriculture and the future of food. There is not a person on the planet that doesn't understand the importance of food, ag, and farming. Couple these facts with people living longer than ever, and we just keep having babies and you have a pot ready to boil over. All of these factors combined will lead to a more crowded planet than we've ever experienced before. With more mouths to feed, we as a global society will need to figure out how to produce more food.

Count data are often used in recommender systems: they are widespread (song play counts, product purchases, clicks on web pages) and can reveal user preference without any explicit rating from the user. Such data are known to be sparse, over-dispersed and bursty, which makes their direct use in recommender systems challenging, often leading to pre-processing steps such as binarization. The aim of this paper is to build recommender systems from these raw data, by means of the recently proposed compound Poisson Factorization (cPF). The paper contributions are three-fold: we present a unified framework for discrete data (dcPF), leading to an adaptive and scalable algorithm; we show that our framework achieves a trade-off between Poisson Factorization (PF) applied to raw and binarized data; we study four specific instances that are relevant to recommendation and exhibit new links with combinatorics. Experiments with three different datasets show that dcPF is able to effectively adjust to over-dispersion, leading to better recommendation scores when compared with PF on either raw or binarized data.

Point-of-Interest (POI) recommender systems play a vital role in people's lives by recommending unexplored POIs to users and have drawn extensive attention from both academia and industry. Despite their value, however, they still suffer from the challenges of capturing complicated user preferences and fine-grained user-POI relationship for spatio-temporal sensitive POI recommendation. Existing recommendation algorithms, including both shallow and deep approaches, usually embed the visiting records of a user into a single latent vector to model user preferences: this has limited power of representation and interpretability. In this paper, we propose a novel topic-enhanced memory network (TEMN), a deep architecture to integrate the topic model and memory network capitalising on the strengths of both the global structure of latent patterns and local neighbourhood-based features in a nonlinear fashion. We further incorporate a geographical module to exploit user-specific spatial preference and POI-specific spatial influence to enhance recommendations. The proposed unified hybrid model is widely applicable to various POI recommendation scenarios. Extensive experiments on real-world WeChat datasets demonstrate its effectiveness (improvement ratio of 3.25% and 29.95% for context-aware and sequential recommendation, respectively). Also, qualitative analysis of the attention weights and topic modeling provides insight into the model's recommendation process and results.

The phrase "artificial intelligence (AI)" carries as much heft as any in our marketing lexicon in 2019. Innovation surrounding AI and machine learning has flourished over the past decade. There is seemingly no limit to the opportunities and potential for AI to impact and improve the customer experience. Despite how far we have come in this area, especially as it pertains to marketing and advertising, we have still only scratched the surface. Consumers can only adopt so much change at one time. Move too slowly and your competition will begin to capitalize on the wealth of data available.

This paper introduces Seeker, a system that allows users to interactively refine search rankings in real time, through feedback in the form of likes and dislikes. When searching online, users may not know how to accurately describe their product of choice in words. An alternative approach is to search an embedding space, allowing the user to query using a representation of the item (like a tune for a song, or a picture for an object). However, this approach requires the user to possess an example representation of their desired item. Additionally, most current search systems do not allow the user to dynamically adapt the results with further feedback. On the other hand, users often have a mental picture of the desired item and are able to answer ordinal questions of the form: "Is this item similar to what you have in mind?" With this assumption, our algorithm allows for users to provide sequential feedback on search results to adapt the search feed. We show that our proposed approach works well both qualitatively and quantitatively. Unlike most previous representation-based search systems, we can quantify the quality of our algorithm by evaluating humans-in-the-loop experiments.

Not all voice assistants can handle the same requests. We put Siri, Alexa and Google to the test. Personal digital assistants – the voice-based interfaces that started with Apple's Siri – were supposed to enable entirely new and significantly more intuitive ways of interacting with our devices, but they ended up being so frustrating that many people gave up on them. Thankfully, we're starting to see some significant advances in phone-based digital assistants, and recent developments from both Microsoft's Build and Google's I/O (and likely from Apple's upcoming WWDC) developer conferences all highlight the important progress that has been made. These conferences, primarily intended for software developers to learn about new advances in the host company's technology platforms, also serve as a great guidepost for consumers to understand how technology is evolving.

People care about their privacy, but not enough to quit buying gadgets that expose their personal data, says a new study on consumer habits. Consumers International and the Internet Society surveyed thousands of people across North America, Europe and Asia to better understand the relationship between consumers and'smart' devices -- a term they defined as'everyday device and products that can connect to the internet.' The study did not include phones and mobile apps, which present a vast and more complex array of privacy issues. What they found was that many respondents using products like Google Home or Amazon Echo, fitness wearables, gaming consoles and internet-connected home appliances shared concern over how those devices harvest and share their personal data. Consumers think that data collection of their smart devices is'creepy' according to a new study of consumers across the world.