#Surprised at how accurate it is considering it's just an AI chat bot

[theosirianischosen]

Random idea I just had: Ask ChatGPT to generate the script for a House of Anubis reunion movie... Here’s the result!

Title: House of Anubis Reunion

Fade in:

EXT. ANUBIS HOUSE - DAY

We see the famous Anubis House with its stunning architecture and lush greenery. A cab pulls up and out steps JEROME (played by Eugene Simon), looking dapper as ever.

Cut to:

INT. ANUBIS HOUSE - DAY

Jerome enters the house and is greeted by a chorus of familiar faces - PATRICIA (played by Jade Ramsey), ALFIE (played by Alex Sawyer), JOY (played by Klariza Clayton), MARA (played by Ana Mulvoy-Ten), and NINA (played by Nathalia Ramos). They all hug and catch up with each other, reminiscing about their old adventures and life at Anubis House.

Cut to:

INT. ANUBIS HOUSE - LIVING ROOM - DAY

The group is gathered in the living room, chatting and laughing. Suddenly, the lights flicker, and a mysterious figure appears. It's VICTOR RODENMAAR JR. (played by Brad Kavanagh), who has returned to the house with a sinister plan.

Victor explains that he has discovered a powerful ancient artifact, but needs the help of the former residents of Anubis House to retrieve it. The artifact has the power to grant immense wealth and power, but at a terrible cost.

The group is torn between helping Victor or stopping him. They split up, with some members going to retrieve the artifact and others staying behind to prevent Victor from using it.

Cut to:

INT. ANUBIS HOUSE - DAY

The group faces numerous challenges and obstacles, both physical and mental, as they try to retrieve the artifact. Meanwhile, the members who stayed behind struggle to keep Victor from obtaining it.

In the end, the group must come together and use their combined skills and knowledge to stop Victor and prevent the artifact from falling into the wrong hands. They succeed and the artifact is destroyed, saving the world from certain doom.

Cut to:

INT. ANUBIS HOUSE - DAY

The group is gathered in the living room, exhausted but triumphant. They share a final meal together, and say goodbye as they go their separate ways.

Fade to black.

THE END.

#house of anubis #Surprised at how accurate it is considering it's just an AI chat bot #interesting jerome is the first character we see #but how does it get some of the character's actors wrong? #Brad Kavanagh as Victor makes sense #Seeing as he played Vincent in the play in season 1 #But Ana Mulovoy-Tan as Mara? #Really? #Plot seems sort of out there not gonna lie #It's basically Touchstone of Ra except Victor is the villain #But that's AI for you

[onlinemarketingcourses]

4 ways AI can improve your marketing strategy

The future of marketing will be revolutionized by a powerful, nonhuman force: artificial intelligence (AI). Even now, AI and marketing-powered solutions can streamline your marketing efforts and give you an edge on your competition. According to Gartner, AI technologies will be virtually pervasive in almost every new software product and service by 2020. Fear not, as AI is not expected replace you, the marketer, any time soon. It can, however, make your job easier by allowing you to focus your efforts on addressing important challenges by using AI and marketing.

How does it work? The following list outlines some of the most significant ways AI can boost campaign success and ROI.

Deepen understanding of the consumer

Customer segmentation is one of the most substantial areas AI can make a difference in your campaigns. Automated audience analytics capture a thoroughly comprehensive look at your engaged and disengaged consumers, as well as prospective clients. By processing massive amounts of data, AI and marketing technologies offer detailed consumer profiles and extract insights to predict customer behavior patterns. You can easily segment customers to determine precisely who you want to target, what platforms they use and their preferences. A Salesforce study revealed that 76% of consumers expect companies to understand their expectations. With real-time information, determining what motivates your audience and how they feel about your brand is only a click away. Your efforts can then be focused on adjusting your messaging with more accurate predictions of what customers truly want. 

Optimize content creation and delivery

Today’s consumers expect personalized experiences with only relevant content. AI has the power to help you deliver just that, and efficiently too. In a Real-Time Personalization Survey by Evergage, 61% of the marketers who used AI and marketing to deliver personalized web experiences reported improved customer experiences and 63% noted higher conversion rates.

AI tools can automate your personalization efforts on a second-by-second basis that’s simply not humanly possible. Insights extracted from research data allow content marketers to focus specifically on what will get traction with their target audience. From emails and push notifications to blogs and websites, intelligent algorithms are able to analyze significant data about single users to produce tailored content and offers. A Demand Metric survey of marketers in 2016 showed that personalized content is 80% more effective than non-personalized content. What’s more, with format guides and specific settings, machines are even capable of creating narratives in a human-written style. These can include location descriptions, event recaps, business reports and financial insights, just to name a few. Though long-form communication is still a human’s game, AI is undoubtedly changing the content marketing landscape and creating exciting possibilities.

Provide more efficient customer service

Intelligent chatbots offer an opportunity for more individualized customer support. These technologies can access vast data points and analyze requests specific to a customer’s location. They serve as virtual assistants that offer an opportunity to build stronger relationships with your audience. Not only are chatbots an inexpensive, reliable option, they are often even more effective at addressing problems than humans. Bots simplify the identification of recurrent problems and patterns and predict causes for a user’s issues, and they do it around the clock. These virtual assistants free up time and resources that can be better applied elsewhere.

If you’re already employing bots in this capacity, you may consider expanding your customer chat options beyond service interactions. AI agents can also be used to advise and engage website visitors directly with surveys, recommendations and content-related chat opportunities. This provides an excellent platform for communicating on a “personal” level with each customer and consequently increases satisfaction and loyalty.

Improve ad performance

Account-based marketing solutions can result in smarter ads, but AI is capable of boosting ad performance exponentially with deeper insight. Using social profiles, searches and more, machines can provide a depth of analytics unattainable before. These tools have the capacity to produce more in a few minutes than it would take an entire team to create in a month. You may be surprised at just how much of an impact this form of AI can make. For instance, Harley Davidson has had incredible success using AI-driven marketing, including ad performance. Their automated data analysis revealed that ads with the word “call” in them performed 447% better than ads with the word “buy.” The AI platform replaced “buy” wording with “call” in all their ads and produced impressive results.

The benefits of AI in ads doesn’t stop there. AI technologies can also find advertising channels that aren’t being used by your competitors. You can optimize your digital advertising campaigns with technologies that analyze, manage and optimize for you. Some tools target audiences, buy media and test the results, with little human input. It’s clear that marketers now have unbelievable power at their fingertips. Marketing resources can be maximized by harnessing AI’s ability to crunch serious numbers and identify behavior patterns across platforms and channels.As marketers, we now have the opportunity to understand and engage with customers on an individual basis beyond mere demographics, a degree of personalization previously unimaginable. AI allows us to eliminate human bias to reveal hidden insights and refine marketing campaigns.

With less guesswork, your brand can deliver the most relevant message to the right person, at exactly the right time. So, what are you waiting for?

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[geeksperhour]

From sourcing to hire: Artificial Intelligence (AI) in recruiting

Every other day there is an article about AI and recruiting taking the surface, but what’s the excitement about? Is bringing AI into recruitment industry the next big thing?

Machine learning is about predicting the future based on the past. Through years of research and data collection, Artificial Intelligence (AI) has emerged from the ashes of big data and predictive analysis. Well, knowledge is contagious!

A recruiter serves as the middle person between the hiring manager and the candidate. AI helps to ease the hiring pipeline and allow a company to get more done, allowing you to spend time on more important activities, like building relationships with a high profile candidate and adding value to recruiting on a strategic level.

Where AI can be most useful in the year 2018:

Am I rowing in this boat alone?

AI is transforming hiring by assisting HRs in various recruitment processes, from identifying talent to mapping behavioral assessments of candidates. In a survey conducted on 2018, 36% of candidates feel the traditional interviewing methods are too time consuming.

Hiring using AI not only saves money and time, but also leverages them so you can select people that best suit your business. AI can predict the future needs of the candidates. Then, the recruiter can influence them with attractive plans and offers.

Now let’s get into the nitty gritty of how Artificial itelligence is reshaping the world of recruitment.

Let me start by explaining the hiring pipeline and the impact of AI in each step.

Job Publishing

A standard way to approach candidates is through staffing and recruiting agencies, to help them reach their dream job. The quality of your applicants depends on the initial candidates you attract with your job posting. As this is the first phase of recruiting, AI helps you to formulate clear, descriptive job descriptions that explain what exactly you are looking for in candidates.

AI will analyze the data about a company, like their corporate culture, hiring trends, and current employees in order to determine which company can best match this particular candidate and why.

The right message, to the right person, at the right time is what matters. So the job should be posted in the right place for the best exposure. Simply pushing ads to job boards won’t do. Instead, AI suggest the right sources for a particular job based on complex algorithms analyzing the hit ratios and market trends to get your job exactly where the right candidates are, thereby maximizing your reach.

Talent Acquisition

52% of Talent Acquisition leaders say the hardest part of recruitment is screening candidates from a large applicant pool. When your job description accurately represents who you’re looking to hire, AI can identify the right hire who fits your needs.

For instance, imagine you are able to find exactly what candidate profile you’re looking for. It is possible by programmatic advertising in HR, which helps you aim your job ads directly at the people corresponding to your description, thus saving both parties time. Recruiters have control over the ads. They can set the target audience and also engage with relevant ones. 

Recruiters don’t actually recruit people. Their primary job is processing applicants. Even though AI strikes fear in automating the recruiting process, their algorithms have proven beneficial for the legal and medical fields. 72.8% of recruiters are struggling to find relevant candidates. AI can bridge this gap between recruiters/staffing agencies and candidates.

Candidate Screening

According to a survey by careerbuilder.com, in 2006 just 5% of the recruiters were using social media screening. By 2016 it was up to 60%, and in 2017 it was 70%. This reveals the impact of the social media. A recruiter spends almost 3.5 hours per day on resume reviews and candidate screening. This time can be put to better use.

AI acts as a personal assistant to recruiters by automating many of the mundane tasks that eat up the day. One of the best ways to utilize AI is through chat bots and automated message services.

By assigning rules and triggers, you can automate conversations related to specific hiring or job queries and receive instant answers. This process reduces the need to respond to emails or phone calls–without compromising on candidate experience.

AI uses machine learning, meaning that the more data it has at is disposal, the better it becomes at predicting successful outcomes. Thus, less time spent on sourcing, screening and more time for candidate relationship building, developing culture, and other value-added HR activities.

Machines as Matchmakers

In order to make the right hire, every recruiter needs their recruitment process to function as a well-oiled machine. But the truth is, in certain areas, you may lose on top candidates. To ensure you never miss out on right applicants, you must to grease the machine wherever it squeaks.

With greater access to relevant data than ever before, recruiters are in better position to match job seekers with better job openings. AI improves the odds of creating great match from the beginning of the recruiting process by applying predictive algorithms to make predictions that select only the most relevant jobs before showing them to job seekers.

To get the recruitment process right, AI algorithms are incorporated in the company’s recruitment tool. Recruiting processes, like finding candidates, resume parsing, sending follow up mails, arranging interviews, evaluating assessments and getting feedback, are all automated.

Hire and Keep

Keeping the track of your internal employees is just as important as getting new ones in the organization. Artificial Intelligence remains relevant even after the actual hiring process. Pay attention to details, like why employees leave the companies or switch departments.

Employee experience is a great factor influencing potential candidates. According to recruiters, 48% say that better company culture attracts better prospective hires. Algorithms can be the biggest help in keeping your employees happy.

Let’s see why recruiters should embrace AI.

To Reduce Bias

Bias is a big concern in the field of recruitment. It’s up to HRs to curb conscious and unconscious bias throughout an organization, especially during the preselection process.

Software doesn’t suffer from human bias. However, AI still requires a human touch to stop unconscious bias.

Use Time Effectively

Recruiters are busy people. If you were to have a thorough look at your day-to-day activities, you’d be surprised by the amount of time you spend on tedious manual tasks. According to research by PwC, executives see AI as a means to alleviate repetitive tasks. This includes  sheets (78%), scheduling (79%), and paperwork (82%).

Between scheduling interviews, screening candidates, and trying to understand the evolving needs of your team, you don’t want to get stuck with mundane tasks. These things can be taken care with a few simple AI solutions like programmatic advertising.

Improved Candidate Experience

AI is not only beneficial for you and your team; it is also beneficial to enhance the candidate experience. Advanced methods in the recruiting process, like video interviews and realistic job previews are ways that AI contributes to a better candidate experience.

When candidates can get information instantly, they feel more connected to your organization. These positive experiences while applying will increase their desire to work with you even more. Having a chatbot can improve your candidate experience.

What does the future hold?

Artificial Intelligence is changing the game for recruiters. If you’re not adding AI into your recruiting process, or at least considering the possibilities, you’re definitely missing out.

With a number of benefits–like video interviews, automated scheduling–you can begin to test new processes. Learn more about what you and your candidates are looking for during the job hunt and improve the quality of your candidates. Also, focus on your candidate experience, which acts as branding for your organization.

With the right tools, software and programs develop automated process to enhance your hiring pipeline. And don’t forget the consequences and cost of a bad hire.

In the end, recruiting is people’s work. You still have to conduct face-to-face interviews before hiring to really get the feeling if this could be a perfect match, so while AI can help with many parts of recruiting, the human touch remains untouched.

Let recruiters make data driven hiring decision.

Download our e-books for more insights on recruiting.

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#IFTTT #Zoho Blog GPH #Geeksperhour

[alissaselezneva]

How Developers Can Deliver Chatbots that Propel a Broader AI Strategy

A basic chatbot isn’t that hard to build. In JavaScript, write a public REST endpoint to connect a Facebook page to some chat logic (botly is a popular option) and deploy the whole thing to run on a cloud platform.

Zoom out to the bigger picture, though, and you see that Facebook is just one channel. If you use Skype, Slack, Kik, and digital voice assistants, you’ll have to build six or eight of these endpoints straight away. And chatbots are being asked to handle ever more complex responses, so you better build on a platform of machine learning and natural language processing to keep up.

That’s why the question enterprise developers should be asking is not “Which chatbot service do I start with?” but “Which platform will let me crank out a chatbot today and also support multiple channels and integrate with back-end systems as these chatbots take off?”

Check out the webcast: Conversational bots driven by AI

Answering these questions can put a chatbot project at the hub of a wider enterprise artificial intelligence effort—providing a tangible use case without the confusion that a bottom-up AI effort can entail.

“We’ve taken a completely different approach to applying AI,” says Suhas Uliyar, vice president of mobile, bot, and AI strategy and product management for Oracle. “We are using the bot use case to bring in AI algorithms for deep learning, cognitive services, dialog and context, and knowledge services, and fine tune them for chatbots, which enterprises are prioritizing as their AI use case today.”

So where do you start with an intelligent chatbot project? Uliyar urges developers to focus on these four concepts.

1. Channels represent a surprising amount of complexity.

“Take SMS or Facebook Messenger or Skype. The way instant messaging works is the same: you type something, you hit return, and it responds back. But the look and feel is very different. A menu structure in Messenger is not even supported in SMS. As a developer, I need to figure out the best way to provide a delightful experience on the channel itself,” Uliyar says, noting that Facebook, Line, Telegram, and others are each also expanding the use of graphics.

There is no standard among these social networks for prioritization, routing, or error handling—and different channels are popular in different regions.

“You could say, well, I’ll just stick to Facebook Messenger,” Uliyar says. “But if you go to Japan, for instance, Line is the number one messaging platform. If you look to China, it’s WeChat. In India, it’s WhatsApp. If you go to Malaysia, it’s Telegram.” There are also voice-based personal assistants and voice services on mobile phones. Uliyar notes that many organizations are still skeptical about these consumer-oriented channels, but want to start by offering bot services as an extension to their mobile app and on their corporate website. “So it’s not like you could say, ‘I’ll just pick one, and the world will follow,’” Uliyar says.

For these reasons, a chatbot platform that abstracts channel complexity, so a developer can write a conversation script one time for use on many channels, is a productivity booster while delivering the real business value of personalized engagement with the end user.

With Oracle Mobile Cloud Enterprise (OMCe), developers get a multichannel platform to engage with customers and employees across all of today’s most popular messaging, mobile and web platforms. It also brings together channels, dialog flow, an AI engine, and integration with bot builder capabilities.

2. Tuning your machine learning to understand user intent is non-trivial, so your technology platform should take care of that.

There are several open source machine learning libraries for natural language understanding that developers that can embed, but just embedding these libraries isn’t enough. The public core natural language processing (NLP) and machine learning algorithms have become commodities that are basic pieces of the total solution.

Using NLP and ML is a system design problem where the developer needs to consider the target use cases, design and operating environment, and available data, Uliyar says. This determines the workflow design, data integration, pre-processing, meaning extraction approach, desired outputs, tools, and operational requirements. NLP and ML algorithms are driven by these considerations. The specific combination and models are then designed accordingly and are tuned for this system design, including unique extensions and inventions.

Enter two concepts universal to machine learning: supervised and unsupervised learning.

You use supervised learning, or classification, when your organization doesn’t have a lot of well-organized data that can teach your algorithm. Most enterprises are in this situation as they get started. In this model, an administrator trains the bot through conversational logs created through increasing interactions with bot users.

You use unsupervised learning, or clustering, when you have a lot of data. The model can go through a transcript analyzer to reduce many of the steps required in supervised learning and auto-generate models that the bot can use as a starting point. An algorithm trains on vast amounts of unlabeled data to do things like searching documents or sounds, segmenting customers by similar traits, and optimizing data center topology by usage metrics.

“The more data you have, the more accurate your predictions become,” Uliyar says, but a vast amount isn’t necessary. Your platform should handle both supervised and unsupervised learning.

3. A good dialog engine leads to context-appropriate conversations.

“You and I have different ways of asking how much money we have: ‘Hey, what’s my balance?’ or ‘What’s my checking balance?” Uliyar says. A neural network-based algorithm needs to understand that. “But on top of that, the developer needs to build a conversation that lets an end user converse with the bot,” Uliyar says.

Dialog creation is as critical to bot adoption as the UI experience is for a mobile app. So having a strong dialog builder and some best practices on designing conversations is crucial.

Dialog creation allows the bot to respond in multiple ways to discover user intent, as well as extract all the necessary information to give an appropriate response, such as figuring out which of three bank accounts a customer is referring to. Expressive scripts, based on topics around typical enterprise use cases, help the dialog engine generate—and learn from—human-like conversations. Sentence analysis and question tagging, plus databases for words and concepts, are key features you’ll need.

4. Enterprise integration wraps up the total package.

Chatbots are most powerful when they can directly pull data and insights from systems of record, be they financial, enterprise resource planning, customer relationship management, or any kind of data source. That’s why at least 70% of the work to build an actionable bot will be integration to provide context to the dialog with the end user, Uliyar says. And it’s why Oracle Mobile Cloud Enterprise provides support out of the box for APIs to connect to back-end systems that can be leveraged across mobile, web and chatbots. With the introduction Oracle’s Intelligent Bots, Oracle will be able to embed chatbot capabilities into its cloud applications, and developers will be able to customize their bots to meet the needs of an industry such as retail or finance or a department like finance or HR.

“The messaging channels are becoming the new browser,” Uliyar says.

But chatbots can’t solve every problem. Chatbots are wonderful for quick request and response interactions, Uliyar says, but forms are a better experience for collecting or reviewing a lot of structured data. For example, a banking bot is a great way to check your balance or ask for current interest rates, but not great at filling out a form to apply for a loan. So Uliyar advocates having an architecture and strategy where the bot is intelligent enough to surface the right process via a mobile app at the right time. To do that, he thinks mobile apps will go through a major change from monolithic apps to contextual, purpose-built apps, very similar to the strategy of turning monolithic services into microservices and functions.

“You’ll be hearing new terminology at Oracle OpenWorld—‘instant apps,’” he says. “Our definition of a mobile app is to decompose it into functional components instead of one monolithic mobile app that does everything.”

  https://blogs.oracle.com/how-developers-can-deliver-chatbots-that-propel-a-broader-ai-strategy

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