AI has begun to disrupt and improve most industries and it's even improving the hiring process, here's five ways it will change recruitment.

Data and AI are not only helping companies to compete but also infiltrating every part of our lives. One area that AI has already made a definitive business impact is in talent acquisition. AI can help automate tasks for recruiters and provide them with insights they have never thought of. According to Korn Ferry Global Survey of 770 acquisition talent professionals, 63% of the respondents mentioned that AI has changed their ways of recruitment while 69% believed that AI has enabled them to find more qualified candidates. A later survey by LinkedIn on Global Recruiting Trends highlighted AI as one of the four most important recruiting trends in 2018, particularly useful in sourcing, screening, and nurturing candidates.

As more data is becoming available, the use of AI in recruiting will only increase. Here are four fascinating use cases of AI which will make recruitment a more effective process for your business.

Better job advertisements

An effective candidate search starts with a good job advertisement. Leveraging the power of natural language processing and predictive analytics, companies such as Textio can inform the recruiters what types of phrases and language patterns would generate better responses and more qualified candidates for the positions, saving time for both recruiters and candidates. Another area is programmatic advertising where job advertisements can be more targeted to the candidates based on their browsing history.

Candidate sourcing automation

Finding candidates from multiple sources and narrowing down the right ones are both challenging and time-consuming. AI can now automate the sourcing process by analyzing hundreds of millions of social media profiles and sending customized messages to engage the candidates, thus helping your company to build a pipeline of talents.

An important source of hiring naturally comes from your own employees. Having an effective employee referral program can save time and money for the recruiters to identify the suitable referred candidates for the hiring managers. In fact, AI can call out your best passive talents within your company and get them to refer to their collective network.

Candidate engagement

Modern consumers mandate speedy transactions. A recent survey found that 69% of all consumers prefer chatbots in engaging with businesses. The prevalence of recruiting chatbots in collecting candidate information and engaging candidates in conversation means human recruiters have more time to spend with the selected candidates for a more thorough evaluation. Recruiting platforms such as Jobvite help for-profit and nonprofit organizations to target the best talents and engage them with personalized messages.

Comprehensive candidate evaluation

The use of video interviews is rising and has two major benefits: It saves the recruiters' time and helps them to review the candidates' traits and characters more completely. With facial and speech recognition software in video interviews the candidate's body language, the use of words, the tone of voice as well as honesty and energy levels can all be analyzed to see if the candidate is the best fit for the role.

Diversity in hiring

Diversity is not only a major trend in recruiting but also something both the employers and employees embrace for a more innovative, inclusive and productive workplace. The use of unbiased and inclusive job advertisements, recruiting chatbots and facial recognition analysis, in addition to tools to assess and hire remote workers contribute to increasing diversity in the hiring process.


AI now appears everywhere and can seem threatening to some. While AI is not going to take over the recruiters' jobs, it is also clear that AI has reshaped the recruitment strategy for businesses in significant ways. AI can augment human intelligence by freeing up humans to spend their time in the most productive way. Recruiters and hiring managers can become smarter and more resourceful in targeting and hiring the right candidate.

What should the company do? Learn and adapt. As AI applications will become more widespread in recruiting, the company can learn from other companies and users and experiment to see which AI tools will benefit its business process the most.

Also Read: What is AI? Everything you need to know about Artificial Intelligence

Read Source Article : Innovation Enterprise

In Collaboration with [HuntertechGlobal]


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The best reading on AI, as recommended by the experts.

Experts are already building a future world brimming with artificial intelligence, but here in the present most of us are still trying to figure out what AI even is. This is a technology that will influence many aspects of our lives, from jobs to entertainment to health care, but that also engages with fundamental questions about what it means to be human. Questions like, “what is the nature of creativity?” and “how do we define consciousness?” Posing the question “how can I understand AI?” is nearly as daunting as asking “what is the meaning of life?”

But as with that tricky life question, a sense of overawing complexity doesn’t mean we shouldn’t try.

In order to help, The Verge has assembled a reading list: a brief but diverse compendium of books, short stories, and blogs, all chosen by leading figures in the AI world to help you better understand artificial intelligence. It’s an eclectic selection that ranges from practical primers to Golden Age sci-fi, and while reading everything listed below won’t get you a job at Google (though it certainly couldn’t hurt), it will give you much-needed context for this confusing and exciting time.

So read, enjoy, and get to know the captivating world of AI a little bit better.


Profiles of the Future, by Arthur C. Clarke 
Graphic by Michele Doying / The Verge

Recommended by Greg Brockman and Ilya Sutskever, co-founders of OpenAI

Profiles of the Future changed our beliefs about how rapidly AI might affect the world. We used to think of technological change as a gradual, slow process — the sum of many small innovations that, when zoomed out, create only the illusion of rapid technological change.

Profiles made us realize there are some highly important exceptions. While later chapters describe Arthur C. Clarke’s predictions about the future, early chapters analyze others’ predictions about technologies like airplanes, space travel, and nuclear power before their development. In each case, the technology was predicted by a small number of optimists amongst a very large, vocal set of genuinely accomplished experts who were confident that a particular dramatic technological advance would never be achieved (at least not on a practical timescale). As a result, even to most experts, massive technological change appeared to come ‘out of nowhere.’

How will long term progress in AI look? Will it follow a predictable trajectory, with the field having a clear view of the upcoming progress in the next 5-10 years, or will we stumble upon a surprising yet disproportionate advance in AI that will transform the world rapidly? The perspective in Profiles means these questions are worth pondering.”


Graphic by Michele Doying / The Verge

Recommended by Rumman Chowdhury, Responsible AI lead at Accenture

“An AI book with no robots, no doomsday scenarios, and no grandiose predictions of the future? How refreshing. This book’s humble and engaging writing style belies a deep hypothesis: the fundamental roots of our current systems of predictive modeling are wrong. According to the authors, we lack a language of causality; that is, quantifiable proof that one thing causes another. This is a fundamental weakness embedded in the history of statistics and tarnishes how we ask questions and seek answers.

The dirty secret of the AI and machine learning methods we use for prediction is that they cannot actually tell us with certainty whether some factor caused another, instead relying on millions of repetitions to give us high-value correlations. Many of our issues of biased outcomes in AI systems stem from an incomplete or poor understanding of interrelated variables (race and zip code, or socioeconomic status and education, for example). While still considered controversial (see Pearl’s debate with statistician Andrew Gelman on Twitter), The Book of Why presents a new narrative that questions and redefines the building blocks of our AI systems.”


Franchise,” from If magazineby Isaac Asimov 
Graphic by Michele Doying / The Verge

Recommended by Tim Hwang, director of the Harvard-MIT Ethics and Governance of AI Initiative

“Asimov’s Robot series is perhaps the cliche reference that gets rolled out when talking about the social impact of artificial intelligence. It’s mostly a convenient excuse to repeat well-worn tropes about The Three Laws of Robotics and point out — sagely — that the dreams of building intelligent machines are long-standing.

But, the cliche misses the mark. In the Asimov oeuvre, it is the stories featuring the massive, impersonal Multivac — rather than the Robot series — that best capture the present day reality of machine learning. In contrast to the walking, talking robots of the Robot stories, Multivac is an unwieldy server farm that requires specialized expertise to operate and frequently produces outputs uninterpretable to the technicians that run it.

One story I’ve found myself revisiting over and over again is Asimov’s ‘Franchise,’ published as a short story in the August 1955 edition of If magazine. In it, a future America (2008), decides to reduce voting to a statistical model that extrapolates the outcomes of all elections based on a set of questions answered by one, extremely representative person.

‘Franchise’ deftly captures the weirdly recursive nature of prediction, and the personal stresses of being the focus of algorithmic analysis. Importantly, the story illustrates the real and tricky balance between predictability and legitimacy. Even if we could do a perfect job predicting voting behavior, or recidivism, or employment performance, what does it mean for this to be an automated process versus a human one? Give it a read.”


Weapons of Math Destruction, by Cathy O’Neil 
Graphic by Michele Doying / The Verge

Recommended by Kate Darling, Research Specialist at the MIT Media Lab

“At first, I wanted to recommend a speculative science fiction book. But sometimes our current reality is a more interesting dystopia. In January 2019, US Congresswoman Alexandria Ocasio-Cortez was ridiculed for claiming that algorithms can be biased. No matter your political affiliation, I think everyone can benefit from a basic understanding of the pitfalls in contemporary AI systems. This book, illustrated with fascinating (and terrifying) real-world examples, is a great primer on the algorithms and data that we’re using, the delegation of power to systems that can make or break people’s lives, and the completely disastrous ways that we get it all wrong. Cathy O’Neil is a mathematician and data scientist who went from academia to the world of Wall Street quants and later joined the Occupy Wall Street movement. Her acclaimed book covers the problems with algorithms in the finance industry, but also in the areas of criminal justice, employment, education, and many more. Many of the AI systems we’re currently deploying and are likely to use in the near future run into the issues that O’Neil highlights. This book should be required basic reading for anyone interested in artificial intelligence implementation.”


The Diamond Age: Or, A Young Lady’s Illustrated Primer, by Neal Stephenson 
Graphic by Michele Doying / The Verge

Recommended by Jeremy Howard, co-founder of

“The ‘Primer’ in the title refers to a leather-bound book. There are three Primers in existence, each one owned by a little girl. The primer is the greatest work of its creator, the top software engineer at the world’s most successful software company. Because, you see, it’s not an ordinary book; it is truly interactive, showing the reader exactly what they need at every moment, described in a way that is designed to maximize their interest. One of the three girls that owns a Primer is the protagonist, Nell, who after finding herself homeless discovers that the Primer has been teaching her all the skills she needs to survive, and to thrive. We follow her journey, guided by the Primer, from a little girl that’s lost everything, to a young woman who may just change the world.

I first read Diamond Age 20 years ago, and this message has stayed with me: technology can be harnessed to give opportunities to those that otherwise would not have them. As with all new technologies, there is today a knee-jerk reaction against ‘screens’ for children. There is no well-designed modern research to support this reaction. If we deny the opportunity to leverage technology in education, then we limit the best education to only those privileged enough to have access to the best teachers.

Our mission at is to help provide access to AI tools and education to all. Technology is vital to this mission. Without it, our users and students wouldn’t have access to our online lessons and community, or the cloud compute platforms we rely on. However, I haven’t yet seen AI used to create a highly customized educational experience like the Primer. The technology foundations are largely in place now; it just needs someone to put them together. When that happens, we may hear of real-world stories like Nell’s.”


Machine Learning for Humans, by Vishal Maini and Samer Sabri 
Graphic by Michele Doying / The Verge

Recommended by Demis Hassabis, co-founder and CEO of DeepMind

“It’s surprisingly hard to recommend books about the nuts and bolts of AI that aren’t either too technical or too philosophical — I predict we’ll see a lot more over the next few years. I’d recommend Machine Learning For Humansas a good introduction that doesn’t require much prior knowledge, plus it’s free online. We were so impressed with it here [at DeepMind] that we ended up hiring one of its authors!

Another way to get to grips with AI is to use a subject you are more familiar with as a gateway. For example, most people know the basics of chess even if they haven’t played it much. Two expert chess players, Matthew Sadler and Natasha Regan, have just written a book called Game Changer about one of DeepMind’s recent research breakthroughs, AlphaZero, which learnt chess from scratch just by playing against itself to ultimately become the world’s strongest player. It’s one of the most comprehensive analyses of an advanced AI program ever undertaken and gives you a fascinating insight into how AI systems like AlphaZero work.”


Sorting Things Out: Classification and its Consequences, by Geoffrey C. Bowker and Susan Leigh Star 
Graphic by Michele Doying / The Verge

Recommended by Meredith Whittaker, co-founder and co-director of the AI Now Institute at NYU

“This is an essential text for anyone grappling with issues of AI bias, fairness, and justice.

Whatever else they are, AI systems are systems of classification. In brief, they ‘learn’ what they know from data, and they use what they learn to classify what they ‘see.’ For example, an AI system for hiring might be taught what a ‘promising job candidate’ looks like by [inputting] videos of ‘successful workers.’ Show this AI system a candidate video, and it compares the video to its ‘successful worker’ composite, classifying the candidate as either promising, or not. Such systems are already in use, and the stakes are high: if, for instance, black women weren’t represented among the ‘successful workers’ training videos, then it’s unlikely the system would classify them as ‘promising’ and unlikely that a black woman would ever get hired.

Sorting Things Out engages with the politics and consequences of such classification practices, treating classification not as a reflection of ‘natural categories,’ but as a product of history, culture, and power in which ‘each category valorizes some point of view, and silences another.’ The book examines classification systems ranging from Apartheid South Africa’s racial passbook, which struggled to apply rigid racial categories to diverse human bodies, to the World Health Organization’s International Classification of Diseases, which requires a vast bureaucracy in its attempt to normalize cultural differences in the understanding of illness and health. By attending to these histories, the authors expose the contingency of categories we often take for granted, providing a foundational resource for understanding, critiquing, and contesting the AI systems that are currently automating classification across core social domains.”


The Master Algorithm, by Pedro Domingos 
Graphic by Michele Doying / The Verge

Recommended by James Vincent, AI and robotics reporter at The Verge

I’m obviously no luminary in the AI world, but as someone who covers this field for a living, I’ve read more than a few books to orient myself, so I do have some experience here. There are two titles in particular that hooked my interest early on and that I continue to recommend: The Master Algorithm, by Pedro Domingos, and Superintelligence, by Nick Bostrom.

Superintelligence is the book about the threat posed by artificial general intelligence, or AGI, written by Oxford philosophy professor Bostrom. It’s inspired some questionable pronouncements from tech leaders on the threat from killer robots (which deserve to be taken with a barrel of salt in my opinion), but is the best introduction I’ve read to the problem of making smart machines safe; a problem which applies whether they’re super-smart or actually quite dumb. And despite the gloomy topic, this non-fiction book is a surprisingly fun read, feeling closer to science fiction at times.

The Master Algorithm, meanwhile, is a broader read that provides an excellent introduction to the technical aspects of AI. It walks you through all the basic components and concepts, from evolutionary algorithms to Bayesian probability, while showing how machine learning as a field cross-pollinates with disciplines like neuroscience and psychology. Domingos occasionally, I think, overstates the raw power of AI (these aren’t magical systems; they’re often deeply flawed, as other books in our reading list illustrate), but even that is a good reminder of how the very potential of this technology can hypnotize

#AI #MachineLearning #DeepLearning #Research #ArtificialIntelligence #Analytics #DataScience #Technology #Marketing #Books

Read Source Article : The Verge

Also Read : 5 Ways to Make AI a Part of Your Marketing in 2019

In collaboration with HunterTech Global


Nowadays, the word AI is no stranger to us. Whether it is a PC or a mobile terminal, whether it is a mobile phone or a car, it does not seem to mean that AI seems to be a little outdated. AI has been closely related to our lives and affects every aspect of our lives.

Although AI is strictly an algorithm, it is inseparable from software and hardware support. The superposition of the two brings a performance gap. 

In order to let everyone have an intuitive judgment on the AI ​​performance of their mobile phones, Ann Bunny officially released the "An Bunny AI Review" public beta, providing a quantifiable standard for everyone to judge the difference in AI performance of different platforms. . 

However, it should be noted that as of the current industry, there is no unified standard for AI, and each chip manufacturer has its own understanding of AI. For example, Qualcomm handles AI related operations through the internal DSP of SoC. HiSilicon is dedicated to AI computing through an internal independent NPU. Samsung and MediaTek also add independent AI chips to the latest generation of chips. Samsung also calls it NPU, MediaTek is called APU. 

In addition to the differences in hardware, there is no uniform standard for SDKs. Each vendor provides SDKs for their own AI chips. Qualcomm's SDK is called SNPE, MediaTek's SDK is called NeuroPilot, and Hess's Kylin SDK is named. HiAI, NVIDIA's SDK is called TensorRT, and Samsung's SDK has not been announced yet. These SDKs will be shown in the evaluation of Ann Bunny AI.

Ann Bunny has established a unified standard for testing through cooperation with the above manufacturers. The test is divided into two sub-items, namely image classification and object recognition.


Is your phone smart?  Ann Bunny AI test release


Among them, the image classification is based on the Inception v3 neural network, the test data is 200 pictures; and the object recognition is based on the mobilenet ssd neural network, the test data is a 600-frame video.


Is your phone smart?  Ann Bunny AI test release


Ann Bunny recommends users to test in Wi-Fi state, although the software installation package size is only 33MB, but the data packet can reach up to 160MB, the user will automatically download the data packet after the first click to start the test (non-Wi-Fi network has Hint) After the download is complete, the packet integrity is verified and the test is officially started.

Only the first time running the Bunny AI evaluation needs to download the data package. The subsequent retest does not need to download the data packet again, but the system will verify the integrity of the data packet every time. If the data packet is damaged, it needs to be downloaded again before testing.


Is your phone smart?  Ann Bunny AI test release


In addition, the size of different platform data packets is not the same. Ann Bunny translates the original neural network into a neural network supported by the manufacturer through the SDK provided by various vendors . Although the packet size is different, the final test sample is identical, and all chips are tested under a uniform standard.

If the chip itself does not support AI-related algorithms, or the SDK provided by the manufacturer can not support the network of Ann Bunny AI for the time being, the image classification and object recognition use TFLite to call the CPU for calculation, which is inefficient and the results are not satisfactory. 

Grading criteria:

1. The score is related to the speed and accuracy. The faster the speed, the higher the accuracy and the higher the final score. 

2, if the speed is faster, but the accuracy is poor, Ann Bunny AI evaluation has targeted penalties to avoid cheating behaviors that reduce the accuracy to increase the speed and ultimately affect the total score, and vice versa.

Special case description:

1, because the bunny AI evaluation is an evaluation of AI computing power, so the AI ​​processor is the same, the score difference performance is not obvious (for example: Qualcomm 845 and 710 DSP models are the same, so the score gap is not large) 

2, currently Samsung has not yet The release of its own AI SDK, so Samsung's own chips are tested by the CPU, resulting in low results, this situation will be improved after Samsung released the SDK. 

3, HiSili HiAI engine object recognition Currently using TFLite to perform calculations on the CPU, resulting in low scores, this situation will also be improved after HiSilicon upgrade HiAI. 

4. NVIDIA's mobile chip can pass the AI ​​algorithm to the GPU through the TensorRT engine through floating point algorithm. 

5, Android version will also have an impact on test scores, in theory, the same chip in Android 9.0 will be higher than Android 8.0, because Google has been optimizing the support of AI at the system level.

Small egg:
After the official version of Ann Bunny AI is released, Ann Bunny will release Ann Bunny AI Mobile Tool at the right time, which is convenient for developers to call the hardware acceleration function of each platform to accelerate the development of mobile AI application industry.

click to download

Read Source Article: Antutu

Also Read : The New Age Enterprise - Enabled by AI

In collaboration with HunterTech Global


Artificial Intelligence (AI) is perhaps the only way marketers today can keep up with customers’ wants and expectations. If you are looking to introduce AI in your marketing activities, we have insights from five MTA experts to get you started.

Artificial intelligence has made giant strides in the marketing functions of organizations in recent years. 2018 saw conversational AI finally gaining mainstream acceptance. In this article, we share five ways, recommended by MTA experts, to introduce AI in your marketing in 2019.

1. Introduce AI in Content Curation

Content curation is an essential area of content marketing. It helps marketers engage users that are in the awareness stage. AI-driven content curation crawls the web and sends personalized emails to users containing news, blog posts and original content.

Content curation also helps to create original content by sifting through piles of content across the internet, helping you shortlist content ideas that are relevant to your audience. 

Lasya Marla (Director of Product, Lucidworks) says, “A sophisticated AI can learn patterns in your content and use those patterns to create more content of the same type. As the ability to create tons of machine generated content becomes more common, a complex and smart system for maintaining high quality in that content will be absolutely necessary. Companies today use AI to create and curate content, discover hidden taxonomies, identify outliers and anomalies and much more.”

2. Run Intent-Driven Ads

Intent-driven personalization analyzes a myriad of data points to understand and predict the customer intent. It helps marketers understand their purchase behavior and the stage of the buyer’s journey they’re in to accordingly decide the future course of action.

AI can help with intent-driven ads by optimizing the creative elements of ads. Daniel Winterstein (Co-founder and CTO, Good-Loop) aptly puts it as, “Intent-driven ads will blur the lines between adverts and advisers.” Daniel further says, “Machines are good at exploring many options: such as trying out different wording, colors, and stock image choices. These choices can be linked to performance metrics, providing feedback to automatically pick the best options - and to train the AI.

Running intent-driven ads helps you find out which audience segments will convert more and optimize ad creatives that are bringing in the maximum results. 

3. Engage Customers Intentionally

Social media gave brands an opportunity to engage with their target audience on a one-on-one basis. Personalization allowed brands to initiate one-way communication through emails and other avenues. Despite making these advancements, brands are still not able to intentionally engage with customers on a mass level. With the help of deep learning, you can now uncover hidden patterns and trends within customer behavior and engage with them on a personal level. 

Nicholas Cumins (General Manager, SAP Marketing Cloud) opines, “Modern consumers are often disconnected due to the pervasiveness of mobile devices, significantly impacting a brand’s ability to break through. Now, deep learning allows brands to form stronger connections with customers by understanding specific behavior and an individual’s propensity to take the next best action. Over time, marketers can analyze these patterns, determine what led to successful outcomes for each individual and use similar strategies for each interaction following.

4. Make Lead Scoring Data-Driven

Lead scoring is a model that helps sales and marketing departments determine which prospects to pursue. Before the advent of AI, this process was performed using human judgment and later adopted a rule-based approach. AI is changing the process by making lead scoring a data-driven process.

Carl Landers (CMO, Conversica) affirms, “In real-world terms, using AI to optimize lead scoring increases the likelihood that they will convert. Where AI really shines is by achieving a fine-grained and nuanced understanding of customers’ responses and interest level in order to feed good information into the system. This delivers an improvement over previous rules- and intuition-based approaches.

5. Offer Superior Customer Experience

As brands prioritize Customer Experience (CX) in 2019, AI will contribute a huge chunk to CX activities at every step of the buyer’s journey. Marketers can extract information from customer behavior and purchase history, which can help the support team deliver better customer experience.

Mark Floisand (CEO, Coveo) recommends, “Having case-relevant information at hand will enable your agents to deliver personalized assisted-support and ensure the process is as frictionless as possible. If your customers don’t find what they’re looking for on your site or app, it could be an indicator that there’s a content gap, which your team can easily address and make available. Or, perhaps they’re looking for a product or service that doesn’t yet exist, which could be great insight to pass to your product development team.


When implemented correctly, AI can project your organization’s marketing efforts to the next level. However, when done improperly, it can stall your growth. To ensure you’re on the right path, check whether you are making any of these mistakes.

Read Source Article: MarTechAdvisor

In Collaboration with [HuntertechGlobal]

Also Read: 2019: The Year AI Goes Beyond The Hype

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An executive guide to artificial intelligence, from machine learning and general AI to neural networks.

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