This is a two part series that dives into the basics for machine learning for human resources and recruitment. Click here to visit Part 1. In a world where the demand for qualified knowledge workers is greater than ever before, highly-skilled professionals expect nothing less than the best from their employers when it comes to workplace benefits, compensation and development for themselves as well as their employees. But, in this highly competitive business world, talent management and development takes a back seat to business plans and meeting ever looming deadlines. In 2015, talent management, specifically employee development is becoming a priority for employers as they re-evaluate the cost of recruiting, hiring and onboarding in comparison to the cost of retaining and developing internal talent within the organization.

Through a special arrangement, what follows is a summary of an article from Retail Dive, an e-newsletter and website providing a 60-second bird's eye view of the latest retail news and trends. Target and three other retailers have partnered with natural language processing (NLP) startup AddStructure to roll out what an AddStructure co-founder describes as a "white-label" spin on Amazon's Alexa virtual assistant, according to a Chicago Tribune report. Chosen in June to participate in the Target TechStars accelerator program, AddStructure has already worked with Target on a six-to-nine-month pilot program and is set to begin similar pilots next month with L'Oreal, Under Armour and the online antiques marketplace, 1stdibs. "We've always been focused on language, so how are people searching for things, what are the priorities around different products through analysis of user reviews," AddStructure co-founder Will Underwood told the Trib. "Now it's just about using that knowledge of natural language to allow people to interact."

Google this week took the wraps off Google Home, its smart speaker and home entertainment hub, but questions remain about the impact the device will have on users' privacy. A direct rival to Amazon Echo, Google Home is the company's entry into the smart home market. Want Google Home to play your favorite song? Just say, "OK, Google" and ask for it to play the music. Want the device, which is equipped with its artificial-intelligence based Assistant, to play that funny cat video from YouTube on your TV? Google Home is always listening for the phrase, "OK, Google" so it can take its instructions.

Major technology companies including Google, Amazon and Microsoft as a team aim at cooperating on best practices of artificial intelligence to help people and society benefit from the technology. On October 5, 2016, these firms which also include IBM and DeepMind in a media announcement disclosed "Partnership on AI" which is a non-profit organization and will help common men and women understand the basics of the technology and encourage them to do maximum practices in the area. Tech titans were concerned about artificial intelligence and believe their step toward the technology efforts will be spinning out of control and eventually become harmful to the world. The firms in a partnership have concluded an outline for the purpose which includes conducting research, recommending people best practices of the technology and publishing research. These researches will be published with an open license while ethics, transparency, fairness, and inclusivity will be some of the areas of the research.

Winterlight Labs, a spinoff from the University of Toronto, is using natural language processing (NLP) and machine learning to identify people with Alzheimer's disease (AD) and other forms of dementia based on patterns in their recorded speech. The World Health Organization estimates that 47.5 million people in the world have dementia, which is defined as a chronic or persistent disorder of the brain marked by lapses in memory, personality changes, and impaired reasoning. In developed countries, AD is one of the most costly of all diseases to treat. Every year in the United States, 236,000 people are diagnosed with AD. There are an additional 100,000 healthy individuals over the age of 60 who seek AD screening.

By acquiring the Viv virtual assistant from the makers of Apple's Siri, Samsung has joined the AI smartphone wars. Now, Google, Apple, and Samsung all have their own AI-driven personal assistant to put at the center of our mobile devices. Apple has been developing Siri as the core of its mobile and desktop operating systems since buying the technology in 2010, and Google's first true smartphone will exclusively have Google Assistant. The message from these companies is clear: just as apps defined smartphones in the 2000s, the next frontier of mobile computing is a virtual entity that helps its owner manage the barrage of information coming their devices. Phones by Apple, Google, and Samsung are similar in design, computing capability, and each have access to roughly the same pool of apps--but each will pursue a slightly different path through their artificially-intelligent assistants. Samsung is one of the world's largest producers of TVs, washing machines, refrigerators, and computers--all of which build an ecosystem that would be well-served by an AI that makes them all a bit smarter, and allows them to work together.

Here we propose a reimplementation of their method and discuss future applications of the technology. Convolutional neural networks (CNN) are now standard neural network layers for computer vision. Transposed convolutions (sometimes referred to as deconvolution) are the GRADIENTS of a convolutional layer. Transposed convolutions were, as far as we know first used by Zeiler and Fergus [2] for visualization purposes while improving their AlexNet model. For visualization purposes let us check out that convolutions in the present subject are a sequence of inner product of a given filter (or kernel) with pieces of a larger image.

The use of artificial intelligence (AI) in the form of artificial neural networks -- in particular, deep neural networks (DNNs) -- is poised to experience exponential growth in a wide variety of embedded systems, but who is going to define, create, and train these little scamps? Before we plunge into the fray with gusto and abandon, it's worth noting that many people think of DNNs only in the context of computer/machine/embedded vision applications. In reality, however, these little rascals are applicable to a wide variety of tasks (see Deep learning hits a sweet note). There are several steps involved in creating a DNN. The first is to define and implement the network architecture and topology.

Companies have another set of tools at their disposal to build chatbots. Salesforce CEO Marc Benioff has begun touting a new LiveMessage service that's aimed at connecting his company's Service Cloud with messaging services like Facebook Messenger and SMS. Benioff is pitching the new service as a way to turn messaging apps into a user interface for Salesforce, in addition to serving as a tool for connecting people with their friends. It will power bots, in addition to direct communications between service representatives and customers. Right now, LiveMessage works with SMS, and it will be expanded to work on Facebook Messenger later this year.

As Salesforce AI morphs from a stand-alone tool to a core element of operational systems, it has the potential to transform the role of traditional enterprise systems like CRM. Rather than serving as static automation systems and systems of record that deliver improved tracking and efficiency gains, new AI-driven systems like Einstein may drive greater marketing effectiveness and revenue growth for companies. It is not easy to hire data scientists who are in heavy demand today. Instead of hiring data scientists to develop models for custom apps, brands can rely on Salesforce AI and other vendors' software with native AI capabilities -- that is, artificial intelligence that is built into platforms. It can take at least several weeks to develop a model for third-party predictive analytics software, so this makes it that much easier to incorporate AI into marketing efforts, and do it in less time.