The New Era is Digital Hiring

We know that people are changing and people have new priorities and ways to make the life more easy, recently bigger companies as Google, Amazon and Sykes are implementing new hiring process using AI, social media and blockchain. This emerging technologies allow to companies reduce processes a half and increase productivity and ROI.

Amazon is the pioneer of digital hiring implementing hiring software and chatbots, this technologies can filter efficiently inclusive in the first interview validating skills and experience with Artificial Intelligence.

What if you could look into the future and know what to expect? So let’s summarize a few exciting developments in the recruiting arena. You can source what you need to optimize your process now and position yourself to enjoy other features the moment you get access to them.

What is Digital Hiring and Which are benefits?

Digital recruitment is the process of leveraging technology to the source, attract, assess, select and hire candidates for vacant positions. This includes leveraging job boards, career websites, mobile recruiting, online assessments, and social recruiting. While most companies these days use at least one of the aforementioned tactics, they are still a long way from being classified as a digital strategy.

Using social media more effectively

Today, no one needs to be convinced that social media is an important recruitment channel. From Linkedin to Facebook groups, even Twitter and Instagram, recruiters are active on social media, posting job ads. But which ones are the most effective? If you’re not tracking your ads, you have no way of knowing.

Using Artificial Intelligence and Chatbots

Just reducing time and hiring efforts,companies can use this resources to improve real problems in the organizations,Chatbots and AI are used in companies as PepsiCo and L’Oreal to improve process. Unlike a human recruiter, Chatbots can make thousands of calls a day, requires no sleep or breaks, and can do in one day what might take a human several weeks. Once completed, she sends recommendations on to the hiring manager for further review. If there’s a problem with the recommendations, hiring managers can alter the questions Chatbot asks to yield better results.

Applicant personality

It is essential for the HR professionals to know about the personality of a potential candidate in order to see how well the candidate can fit into organization culture and policy. Social networking sites such as Facebook, linkedIn, twitter help in obtaining insight details about the applicant professional and personal lifestyle. It is a huge benefit for the companies when candidate use digital CV for a job as it consists of details about the individual precisely. It saves money and time of both the candidate and HR as candidates also get a chance to see company work culture through social networking sites.

Efficiency

Digital hiring is a time saver which improves hiring efficiency drastically. Internet never sleeps hence, instant response to job offer is quite possible. Not just speed it actually offers great services to one who relies on online hiring.

If you’ll put great efforts and go deep in your digital recruitment tool box then one can easily carry out information about previous placements and get much better result by comparing.

Social media hub allow you to track the status of the application and make task bit easier for the HR department. Progress can be monitored and screened from one place with ease.

AI for Hiring Talent

AI for recruiting talent can improve time and efforts of recruiters, this emerging technology now is adapting so fast and can reduce or eliminate time consuming activities as screening of resumes and interviews. Screening process nowadays represent >50% of time wasting in verify resumes and validate skills.

Big Challenge is validate talent efficiently and faster, change the way to hiring talent and automatize job profiles can be benefit of AI. Team leader are interested in change traditional ways to hire talent. Numerous of big companies are recruiting based in cloud database and company records to check last performance, work behavior and skills.

How to improve screening process?

Manually screening still wasting time resources, especially when 70-80% of received resumes not are qualified, job requirements not are clear or simply people apply to job positions just to have a chance. Recruiting process and shortlisting of candidates can estimate more of 24 hours per candidate, it’s a lot time wasting in a candidate.

AI represent a big help to solve repetitive tasks and can identify behaviors, habits, skills of candidates, an advantage of this technology and how can adapt and interpret human features so easy, AI applied in HR can define work conditions metrics and filter resumes by gender, education, work experience,etc.

Interviews can be a bonus with AI, implementing streaming interviews and video record we can analyze human behaviors, emotions, personality and other important characteristics depending of job requirements. Speeding up these parts of recruiting through automation reduces time-to-hire, which means you’ll be less likely to lose the best talent to faster moving competitors.

Recruiting quality depends more of KPI’s that fast method, just measure a performance and reduce inability database loop, storage data is not enough just using efficiently data and improve profiles and candidates information. As data HR now is easy to collect, analyze and standard matching between candidates and job skills as experience, skills and knowledge.

AI Benefits

AI for recruiter promises reduce candidates information that can discriminate for gender, sexual orientation, age,etc. Access of database can improve a real job selection excluding personal beliefs because in a lot of occasions can affect hiring process.

Fast and qualified talent, is the principal promises of AI is reduce time invested to get talent, and validate candidate phase and select a candidate based in filter and profile validations. Fast talent acquisition is an advantage with AI because guarantee automation of process.

Storage data is another benefit of AI because eliminate papers and use cloud to select candidates information, using task and labels to identify and separate candidates depending job descriptions and skills.

Chatbots

Chatbots are currently used by companies to make easy communication between candidates and companies, now chatbots are testing in real time question to validate candidate and providing feedback.

Interaction with candidates can be improved with bots because companies can to collect information efficiently and creating community with interaction, optimize data with chatbots using another tools in cloud to analyze this information.

Predictive Analytics

Data can improve results with big data and compare with past outcomes and predict such as job performance and application engagement,using data efficiently we can better predict future outcomes, Define skills, attributes and patterns we can predict models and work behaviors.

Use data correctly can improve productivity, applying big data and AI recruiters can manage candidates skills, interviews, job ads and inclusive work performance through performance record.

Reducing Hiring Bias

Data collected and analyzed by AI help to solve KPI’s metrics and eliminate some of the most problematic human flaws in the hiring process. Reducing hiring bias is a big challenge for companies, Implicit or explicit method used today are old just eliminating common problems as collect, analyze and selection of data we can improve results elimination bias and personal stereotypes that can discriminate qualified talent.

How AI help for hiring talent?

Candidate Classification targeting

Many companies do not have a pattern to pot job ads, not describe enough jobs requirements, probably your jobs ads are reading for wrong crowd. AI improve this process with customization profiles and description, Facebook and LinkedIn are using marching learning and AI to have access to targeted audience. Look for software platform that feature artificial language processing and job taxonomies that can convert your entire job description text into a data model that can be used to target your ad the right candidates on a specific time and site.

Applicant Resources

AI can create followers community, technologies can identify hobbies, skills, labels and others important information of each candidate. Knowing personal information and collecting in database the companies have an interaction with possible applicants. Providing good content and interest topics we have possibility to improve relationships and create a company branding

Efficient Reports

Solutions that offer real-time reporting and predictive insights will fix your data woes and help you measure effectiveness from beginning to end. AI solutions offer a new level of data transparency that allows easily measure ROI across all of your recruitment spend, set better expectations internally, and even improve how and when you allocate your sourcing resources to fill your open positions fast.

Chaotic Communication

AI can improve interaction between recruiters and candidates, chatbots and another software tools and CRM have benefits to accelerate hiring rates, use them to improve communication and have a relationship with clients and your most important human resources. Look for a way to automatize with easy ways as email marketing, chatbots, interviews streaming,etc.

AI is an excellent tool for recruiter to get great talent in a halftime that conventional strategies, automatize process and improve your hiring activities.

If you want to more information about how use AI and another technologies in hiring process, please check https://4geeks.io/teams

Here’s a Demo to Implement Google Cloud Video Intelligence API

Some months ago Google showed to the world how to use their most recent video intelligence product, Google Cloud Video Intelligence API.

Ok, to get context, Cloud Video Intelligence is basically an API that makes videos searchable, and discoverable, by extracting metadata with an easy to use REST API.

You can now search every moment of every video file in your catalog. It quickly annotates videos stored in Google Cloud Storage, and helps you identify key entities (nouns) within your video; and when they occur within the video. Separate signals from noise, by retrieving relevant information within the entire video, shot-by-shot, -or per frame.

Take a look at the following video to understand the power of this tool.

If you working with videos, take your time to learn more about this API. If you are not working directly with videos, feel free to share this post with your boss… maybe it can be useful for next business strategies.

A Neural Network Playground In The Browser

Well, maybe Neural Network can be a fancy or very new term. The thing is that Neural Network is the “base of” Machine Learning, and other applications like Artificial Intelligence.

A Neural Network it’s a technique for building a computer program that learns from data. It is based very loosely on how we think the human brain works.

First, a collection of software “neurons” are created and connected together, allowing them to send messages to each other.

Next, the network is asked to solve a problem, which it attempts to do over and over, each time strengthening the connections that lead to success and diminishing those that lead to failure.

Web TensorFlow Playground

So, this web tool is an interactive visualization of neural networks, written in TypeScript using d3.js. Go to http://playground.tensorflow.org to start playing just in the browser. I tested it on Google Chrome.

BTW, you can find the source here in TensorFlow’s GitHub.

Neural Network Playground, TensorFlow

I would like to know if you are working on some Neural Network project. Maybe you can get the exposure needed 🙂 Use the comments section below.

Real-time diagnostics from nanopore DNA sequencers on Google Cloud (Proof of Concept)

Guys, I want to share with you a blogpost from Google Cloud Blog, about a proof of concept on real-time diagnostics from nanopore DNA sequencers.

In a healthcare setting, being able to access data quickly is vital. For example, a sepsis  patient’s survival rate decreases by 4% for every hour we fail to diagnose the species causing the infection and and intervene with an appropriate antibiotic regimen.

Typical genomic analyses are too slow. You transport DNA samples from the collection point to a centralized facility to be sequenced and analyzed in a batch process, which can take weeks or even months. Recently, nanopore DNA sequencers have become commercially available that stream raw signal-level data as they are collected and provide immediate access to them. However, processing the data in real-time remains challenging, requiring substantial compute and storage resources, as well as a dedicated bioinformatician. Not only is the process still too slow, it’s also failure-prone, expensive, and doesn’t scale.

We recently built out a proof of concept for genomics researchers and bioinformatics developers that highlights the breadth and depth of Google Cloud’s data processing tools. In this article we describe a scalable, reliable, and cost effective end-to-end pipeline for fast DNA sequence analysis built on Google Cloud and this new class of nanopore DNA sequencers.

We envision four scenarios that can use this application, specifically to detect biocontaminants:

  • Medical professionals
  • Veterinary clinics
  • Agronomists
  • Biosecurity professionals

In all cases, analytical results are made available in a dynamic dashboard for immediate insight, decision-making, and action.

Here’s a video of the University of Queensland’s application performing real-time analysis of DNA nanopore sequencer data:

You can read the full article here.

Deconstructing Chatbots

AI and Chatbots are trending topics nowadays, but some people don’t understand how terrific could be a chatbot in business.

The following video shows how chatbots works, can they understand what humans says, and how they can response by using artificial intelligence.

Is your business leveraging chatbots to support clients or maybe your business planing the next move? Take a look.

 

7 Steps of Machine Learning (Artificial Intelligence)

Hi 4Geeks Nation! I want to share with you this video (from Google AI) that explain so clear 7 steps of Machine Learning.

  • Gathering Data
  • Preparing that Data
  • Choose a model
  • Training
  • Evaluation
  • Hyperparameter Tuning
  • Prediction

 

E03: Chatbots

Hi there! Today we are going to talk about chatbots, and how it can help you to engage customers and do better business.  A chatbot basically is a program with capability to understand natural language (English, Spanish…) and trigger orders.

Some of the most popular companies providing chatbots platform are Google (DialogFlow), Facebook (Wit.ai), Amazon (Amazon Lex) and IBM (IBM Watson).

You can subscribe to this podcast on your favorite platform; iTunes, TuneIn, iVoox and YouTube included. Rate us and share with your community. You can always send us your questions by voice 🙂

Hosts: Allan Porras and Sergio Monge.

Chatbots: Are you a human or a bot?

Alan Turing, for more than one a God and from my point of view was the one who triggers this actual concept of machines. It was during 50’s when Turing postulate Turing Test. It was theoretical postulate only and as It’s own concept was lacking of variables we have now, nevertheless, It was one of first human being asking himself if we can determine if a machine is actually behaving as one or most like a human.

It was 1966 and a MIT professor called Joseph Weizenbaum, came up with ELIZAThis bot was able to start a conversation and simulate a Rogerian psychotherapy by most of the cases rephrasing client’s statements.

But everything changed with SmarterChild, why? basically because It changed a little bit the way a bot was perceive. Before, bots was for entertaining only, this bot was able to help you in different ways, like movies or appointments.

The older chatbots just compared patterns and that was everything, they build an answer based on patterns. Now, a bot can analyze data and elaborate an intelligent answer within learning included. This is where it gets interesting …

What is?

All chatbots are powered by NLP (Natural Language Processing), nevertheless, that only allow to us to understand/process inputs, the tricky part is when we answer to those questions, we need real big data.

In other words by it’s own conception, chatbots are useful without a good database where we can process and give an extra value to chatbots. A chatbot without big data is like an app that just show information, it just consumes memory and phone resources without a good unique value.

So, chatbots have use machine learning to process users inputs, this learning can be supervised or unsupervised, everything resumes un patterns which are processed by neuronal networks, which can detect patterns and learn from this. But machine learning is a topic we can go though later.

At the time we tech/feed that chatbot data, more valuable it becomes. Let’s say you have a e-commerce company, with a good chatbot, you can get reports really easily by asking something like:

  • What is my projection in the next 2 years ?
  • What has been my revenue this month ?
  • What is the most buyed item ?
  • Give me the email of our most recurrent buyer.

To answer that question a chatbot will have to go and processed some big data. That’s the importance of a database well fed.

A human is totally able to do what a chatbot is capable to do, at the end of the day, we want to make our lives easier, don’t we? Who wants to perform a search for hours when you can only ask for something.

Future of Marketing

Why?

  • Navigation Assistant
  • Automatic Responses
  • Reactive Communication
  • Recommendations
  • Receive Orders
  • Visit Registration
  • Clients Monitoring
  • Engagement beyond clicks

What are the benefits of chatbots overall

  • Reduction of operating costs and moderation.
  • Increase in response rate
  • Support 24/7
  • Telemarketing savings.
  • Increase in service during peak hours.
  • Automation and simplification of sales
  • Improvement of the customer experience (CX)

Musts

Having a chatbot is a continuous work (like everything in this life), but there is always some rules you have to consider if you want to have a good and realistic chatbot.

It has to sound familiar, like a human been, you can see this approach like easy/difficult, but It’s very important, people have to feel like they are talking with a real person, so, an identity and personality (artificial) for a chatbot is crucial. So, Does it have to be funny, introverted, friendly ? Your choice.

You have to give people reasons to comeback and use your bot again, create a necessity. That’s why it’s important to create a familiar environment.

To continuous improvement on your chatbot you must check logs and look for user needs, and make your chatbot learning faster.

It’s important to create all basics for your bot, it’s like to create a building, you first have to put all the foundations.

Before start a chatbot

  • Objetive

Make a realistic scope of your bot, so, you can have a metrics and check how is your bot going on.

  • Personality

Give to you bot a personality, just like we talked before, It’ll be nice people talk about you bot with specific characteristics and a differentiator from others.

  • Solution

What your bot will solve for your end users. It as to be a key differentiator for your clients.

  • Lifecycle

The most important part. What is the workflow of your bot, what is the beginning and what’s the end.

As you see all those points depend on each other, so It’s important to have all of them well described and decided. Then you are ready to start your bot.

I’m a developer

If you are a developer like me, and you want to start digging into this obscure world, there are some tools that right now does the NLP for you:

  • Cloud Natural Language API — Google
  • Cognitive Services APIs — Microsoft
  • Watson Conversation — IBM

Free for all (for now):

  • API.AI — Google
  • Wit.ai — Facebook

Those tools can help you to create a cool chatbot. At 4Geeks we have used API.AI to play with our Google Home. It’s easy and funny.

So, as you can see there is a list of benefits and and todo-list before start with you chatbot, but, the most important part of this is to have a cool chatbot with excellent data and a well defined goal. I argue you to comment and contribute by adding more and more info.

If you are asking if there is something more and more deep I want to invite you to be in touch, cause later, I will talk about how to create a Neurologic Network with Python to create the basics of a custom bot with self learning.

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