Do you know that you’re missing a great opportunity every day you delay using AI-powered solutions for your content marketing? This means that you’re completely losing a competitive edge over the marketplace! The goal is to reach key prospects when and where it really matters! It’s time for you to capitalize on technological advances such as AI.

 

Does it ring any bell? If it does, then you’re headed on the right track. That’s because AI-technology incorporated in your marketing efforts can advance your customer engagement into massive leaps.

 

Artificial Intelligence will help you craft highly personalized content to target your ideal customers with surgical precision. With the help of AI, it will be easier for your employees to identify, connect, and convert prospects.

 

In addition, AI will help you streamline your marketing processes and prevent unnecessary expenses that hinder your capability to grow your business. It’s a discreet tool that is used by popular brands like Amazon, Netflix, Google, etc.

 

Artificial Intelligence has become really popular in modern marketing. It has helped thousands of brands increase engagement with prospects. Likewise, tools like this which were once available only for enterprise-level companies are now made available and affordable for SMBs.

 

Do you want to learn more about AI and the benefit you can gain by using it? Hop on! We will help you achieve success. So, without further ado, here are five reasons intelligent marketers use AI.

 

Create highly personalized content

 

There’s a huge potential for content marketers if they can work efficiently. One of the best ways to do that is to integrate ‘natural-language generation.’ Several years ago, Gartner, the global research firm predicted that machines would create 20% of all business content.

 

Today, machines are capable of creating content using simple rules and formats like:

 

  • Business reports
  • Profit and loss summaries
  • Actual stock insights
  • Hotel descriptions
  • Sports game recaps

AI-generated narratives are tailored as though it was written by another human. However, the tone and insights of each narrative depend greatly on the rules established by your brand. In addition, Rocco, an AI-powered marketing tool can provide you with fresh social media insights that your followers will like to engage with

 

Artificial Intelligence research reports and ebooks may look like a story straight from science-fiction. However, there are AI tools that you can use to craft an engaging email or social media content, personalized reports, and messages from the data provided.

 

Boost your PPC advertising

 

The majority of marketers choose to assign their pay-per-click budgets to Facebook and Google Adwords. In 2017 alone, Google controls 40.7% of the digital ad market in the US while Facebook holds about 19.7%.

 

Some companies opt to have their pay-per-click campaigns either managed by a PPC agency or in-house. That means it’s managed by human employees. However, AI can discover new advertising channels that your competitors might not know about.

 

With that said, AI is a great tool for advertisers to test more ad platforms to help them optimize their targeting campaigns. On the other hand, that’s exactly what the social media giant, Facebook is doing.

 

You can take advantage of this approach since this can be applied to an omnichannel PPC campaign data using either AI-tools or a third-party. However, using an AI marketing tool requires little human effort while doing important tasks like analyzing, optimizing, and managing paid ad campaigns.

 

Moreover, when working with large-scale PPC campaigns, AI, along with machine learning can help you find or identify new ways of increasing your bids, targeting efforts, copywriting, and layout.

 

Get customer insights faster

 

Crunching the numbers and then matching them with customer behavior would take humans countless hours to achieve. However, if you put AI into the equation, this task can be done in no time.

 

The best example is Dynamic Yield. They were the agency that helped big companies like Sephora, Under Armour, Urban Outfitters, Ikea, among others build actionable sections using advanced machine learning technology.

 

AI algorithms are responsible for creating customer personas from billions of data. This includes location-specific events, on-site collaboration, customer purchase behavior, former conversations, referrals, sources, and psychographic factors.

 

This means that machine learning algorithms will allow you to:

 

  • Clearly identify the right customer segments to include in your campaigns
  • Create accurate matches to products and services that prospects would most likely use
  • Prevents product returns

 

AI can tell you about the most relevant products and contents. This is based on the data regarding the interactions of visitors with your website.

 

Give your prospects a highly personalized website experience

 

While AI is far from creating a new website on its own, what it can give you is the insights to create a meaningful visitor experience for your website. That’s because of its intelligent personalization capability.

 

In a 2017 survey conducted by Evergage, they found out that 33% of marketers who deliver highly personalized web experience are using AI. In addition, when asked about the benefits of using AI’s personalization, 63% said they got increase conversion rates while 61% said their

 

Another interesting report is from Boomtrain. They said consumer brands like Pandora, The Wall Street Journal, Top Fan, and La Redoute are leveraging AI technology not just to boost their conversion rates but it was also instrumental to differentiate their brand from their competition.

 

Likewise, Pandora merged machine learning with human curators to come up with songs that listeners tend to play more. Music listening platforms like this provide the best example of what a competitive advantage looks like. The key is to deliver the best user experience possible.

 

On the other hand, if you’re working with large amounts of content, machine learning algorithms can help you uncover the most relevant information thereby giving your company a competitive advantage.

 

In addition, because you can’t check your analytics every second, AI can manage this task for you. AI tools like Hunch and Slackbot are very capable of analyzing your Google Analytics data while providing relevant insights on general performance and large-scale changes in your statistics.

 

As a result, you can be assured that your website is performing perfectly and you can address any inconsistencies that may arise.

 

Customer-support chatbots

 

Have you ever come across a customer-support rep? That helpful correspondent no matter the name can possibly be a ‘bot.’ Intelligent chatbots are changing how customer-support is done. That’s because in some cases they provide better support than their human counterparts.

 

That’s because chatbots have access to a wide range of customer data. But that’s not all! They have the ability to combine specific location requests, reveal patterns, jot down recurring problems, and anticipate user issues.

 

For this reason, they are considered more knowledgeable compared to human chat support reps. Meanwhile, AI-powered chatbots are very common. But, to get the edge, you can use AI chatbots to create personalized content marketing by using the system as an expert adviser for every visitor of your website.

 

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Mia Clarke is part of the content and community team at Userful.com, experts in all things video wall and display solutions. When Mia is not spreading the word about video walls she is often found discovering the great outdoors, walking or cycling.

 

It is the night before the weekly shop. I look in the fridge and consider my three tomatoes, the sweet potato and the asparagus.

Normally, I’d take this as my cue to nip to the fish and chip shop.

However, I’m trying out Plant Jammer, an app that promises to rustle up a recipe based on whatever food you have lying around, using artificial intelligence.

It searches three million recipes to find often-paired items. It then consults a library of ingredients which the company has hired professional chefs to group by flavour - salt, umami, sour, oil, crunch, soft, sweet, bitter, spicy, fresh and aroma.

Finally, the software learns from this data and devises new recipes.

Future food?

Michael Haase, the founder of Plant Jammer, says this last step is what makes his app unique.

Traditional recipe apps are powered by databases - you list what you have in the fridge and the app sends a pre-existing recipe it found on the web.

"That is the old way," says Mr Haase. "We are actually constructing new recipes from scratch each time with an AI [artificial intelligence]. This is going to be the future."

Plant Jammer is one of a handful of recipe apps, food distributors and even events companies that are turning to artificial intelligence to gain an edge in the food industry.

To make use of my sweet potato, the app suggests several meals including a stew and a fry up.

I chose to make them into vegetable burgers. I tell the app I have no dietary restrictions, then tick off my ingredients. Lastly, it asks what seasonings I might have.

Based on what I have ticked, my sweet potato patties will also include asparagus, aubergine, chickpeas, lemon juice and crushed-up walnuts. I add some seasoning and rolled oats to bind them.

They go into the oven for 15 minutes. The result is four overcooked, and strongly oat-flavoured, discs.

Adjustments

When I tell Mr Haase, he admits that not every recipe is a success and also agrees the recipe probably needed more options to bind the patties together.

An hour later, the platform has changed to adjust for my feedback. I promise to try the recipe again.

Plant Jammer team relaxImage copyrightPLANT JAMMER
Image captionMr Haase (third from right) with the Plant Jammer team

There is a prime membership available which around 5% of users sign up for, paying for the running of the app.

Plant Jammer also sells subscription plans to supermarkets, offering ingredient alternatives to their website recipes.

“So if you want to make it vegan, gluten free or Thai we can adjust any recipe,” says Mr Haase.

He hopes Plant Jammer will offer people the chance to master less-wasteful, vegetarian cooking.

'The hard way'

Even packaged food manufacturers have turned to artificial intelligence.

Analytical Flavor Systems is New York research and development firm that uses AI to advise food companies on improving their products or creating new ones, including drinks.

Its AI platform Gastrograph can predict the flavour, aroma, and texture a drink would need to cater to any regional food preference.

“We’ve done this the hard way,” says founder Jason Cohen, who has spent the past 10 years running taste tests around the world.

Jason CohenImage copyrightBITSXBITES
Image captionJason Cohen says perception plays a large part in the flavour experience

Every day, his panel of 50 tasters try different packaged food products two or three times a day. Before Covid-19, there also had a travelling team visiting a different country each week to test regional preferences.

What people taste is less important than what they perceive when they taste, says Mr Cohen, a former tea sommelier, who adds “perception is a very easy thing to play with”.

“For example, if we add vanilla at about one parts per million to milk, you won't be able to taste the vanilla, but you'll say that the milk is creamier and higher quality,” he explains.

The artificial intelligence software runs through hundreds of decisions until it learns to predict how good a product is going to taste - based on what the product is meant to taste like, panel testing, and regional tastes.

Creative decisions

Using AI to find new combinations of flavours for cupcakes and cocktails put Bristol based media agency, Tiny Giant, on the map.

Co-founders Richard Norton and Kerry Harrison have used AI modelling to help create marketing events, ad campaigns and even gin labels.

With Monker’s Garkel gin, Tiny Giant’s coders fed a computer hundreds of different gin names. The computer analysed the samples so it could invent its own.

Monker's GarkelImage copyrightTINY GIANT
Image captionWould you let AI come up with a name for your gin recipe?

This kind of machine learning is called a neural network - when a computer creates one it will recognise a pattern, like 'what does a gin label sound like or what goes into a cupcake?' - and then make a creative decision about it.

After Tiny Giant’s weekly AI cocktail night got the attention of larger companies, they were inundated with requests from large corporations to host events with AI-generated cocktails and cupcakes.

“I didn't really expect this to become a thing where we would become food creators, but why not?” says Mr Norton.

'Flabbergasted'

Cookbook author and chef Meera Sodha agrees the pairing of AI and food can foster research, creativity and sustainability, but says you cannot “sever a recipe from its story”.

Ms Sodha was inspired to learn cooking after a trip to Brick Lane with university friends.

Meera SodhaImage copyrightDAVID LOFTUS
Image captionMeera Sodha had to record her family's recipes for posterity

"My wonderful intelligent white friends asked me what they should order from the Indian curry house.," she recalls. "I was flabbergasted that they thought that this Indian food was what I grew up eating”.

When she learned to cook from her mother she had a further “huge moment of panic” when she discovered no family recipes were written down.

They would all die with her if she did not make a record of them.

“What I love about cooking the recipes collected from my mum, my grandma or my aunt is that I feel connected to them when I cook that food in my kitchen," she says. "I feel like they are there by my side.”

Nell Mackenzie and Anne Mooney cook with a recipe made up with artificial intelligence
Image captionNell Mackenzie and Anne Mooney cook with a recipe made up with artificial intelligence

In this spirit, I attempt the potato patties once more but this time with my mother Anne Mooney, a former professional chef, over Skype.

But she prefers not to let the app tell her how to cook - using it instead as a spring-board for ideas, particularly the combination of chipotle, cilantro and pine nuts.

We both avoid the oats and fry our patties.

They taste better, but I think this has more to do with our online get-together rather than our command of technology.

Source:BBC

Artificial intelligence (AI) is transforming the world as we know it, especially in the healthcare industry. With using AI in healthcare practices, doctors are able to save lives through better diagnosis and treatment and patients are able to take more control of their healthcare. Below we address the key ways AI is revolutionizing healthcare.

Advancing Diagnostics

Many offices and healthcare specialists are already implementing AI technology to analyze patient’s symptoms for faster, more accurate diagnoses. AI tools are useful in predicting and catching disease early on. Some imaging tools can even diagnose more accurately than humans,

AI technology is also beneficial when it comes to personalized treatment, as it’s easier to provide with time saved from automated tasks. By automating tasks that don’t need to be performed by humans, doctors can focus all of their time and efforts on patient-specific care. 

Improving Treatments

AI can improve treatment. For instance, brain-computer interfaces have been developed to potentially change lives by restoring the ability to speak and move. Likewise, this interface can also change lives for patients who have suffered grueling illnesses, such as strokes or ALS. 

Innovators have been also working to develop new machines to move forward with immunotherapy. These new technologies can help increase the retention rates of patients with immunotherapy, as well as personalizing the treatment to adhere to the patient’s specific needs.

Using AI has also led to breakthroughs in discovering new drugs and medicines for patient care. Artificial intelligence allows for faster and more cost effective discovery rates for medicine. These studies have the potential to save millions of lives and reframe patient treatment as we know it. 

Enhancing Patient Engagement

It’s important to note that AI is not just being used by doctors, but also by patients themselves. A great way for patients to incorporate AI into their daily lives is with wearable technology that is tailored to their specific needs. Examples of wearable technology patients are using include: fitness trackers and smart watches. These tools also gather data on individual health, leading to more insightful analysis. 

Doctors also prefer that patients have some sort of AI system with them to engage them in the care treatments. One of the leading reasons people don’t get better is because they fail to stick to their care plan. Without following doctor guidelines, patients aren’t in the best position to get better.

AI can also be used to create an online platform for patients. A platform that’s accessible at the patient’s fingertips allows them to get the medical advice they need quickly, as well as saving time for doctors and providers. 

Facilitating Administrative Processes

AI works hard to automate certain tasks that take up human resources and time more than necessary. Tasks that decrease productivity include: managing patient files, tracking medical history, and writing out prescriptions. With automation, healthcare officials are able to use the saved time towards actually working with the patient and ensuring as speedy of a recovery as possible. 

Automation can also help reduce the risk of errors in the medical field. Mistakes happen, but in the healthcare industry mistakes are ten times more costly than anywhere else. Sometimes a minor mistake can even cost a patient their life. Humans aren’t perfect and mistakes are inevitable, but AI reduces the risk.

Additionally, medical insurance companies can highly benefit from incorporating AI into their existing workflow. The amount of claims insurance companies receive are endless and many claims are actually marked as fraudulent. AI can alleviate the time needed to flag all of these claims by being able to pinpoint issues in a matter of seconds rather than hours.

 

Article by: 

Julia Morrissey

AIOps, (for artificial intelligence for IT operations) is the application of artificial intelligence (AI) to enhance IT operations. Specifically, AIOps uses big data, analytics, and machine learning capabilities to do collect and aggregate the huge and ever-increasing volumes of operations data generated by multiple IT infrastructure components, applications, and performance-monitoring tools. It also enables the intelligent sift of ‘signals’ out of the ‘noise’ to identify significant events and patterns related to system performance and availability issues. The combined capabilities of ML and analytics help diagnose root causes and report them to IT for rapid response and remediation—or, in some cases, automatically resolve these issues without human intervention.

By replacing multiple separate, manual IT operations tools with a single, intelligent, and automated IT operations platform, AIOps enables IT operations teams to respond more quickly—even proactively—to slowdowns and outages, with a lot less effort.

It bridges the gap between an increasingly diverse, dynamic, and difficult-to-monitor IT landscape, on the one hand, and user expectations for little or no interruption in application performance and availability, on the other. Most experts consider AIOps to be the future of IT operations management.

However, the world of AIOps presents a duality. On the one hand, it’s an emerging technology that for the first time mashes up operations and AI. On the other, many of the solutions in this space are traditional tools that have been updated to leverage AI. This mix of old and new, traditional players and startups, makes this space particularly interesting. According to a report, here are key highlights of the prevailing landscape of AIOps.

The AIOps tools in the market today are on a spectrum with regard to the use of AI. While some make use of knowledge engines systemically in the monitoring and management of cloud and non-cloud systems, most tools leverage AI as an afterthought, not driving much of the functionality of the tool.

Enterprises are typically adopting AIOps as an upgrade to existing ops tools, and are remaining brand loyal. This means that the upstarts in the AIOps space will find it difficult to break into a market where the established players are in essence selling with the same basic message: AI integrated with management and monitoring that you trust. Considering this, we may see a consolidation next year as the market focuses on a handful of players, down from the two dozen or so relevant players today.

There seem to be two directions in AIOps: self-healing and not self-healing. Some AIOps systems are able to heal issues with systems that are managed and/or monitored. This means that if the tool finds an issue, a process is launched to attempt to correct the problem, for instance restarting a server or a network hub. Other solutions are more passive, alerting users about an issue, but without taking automated corrective action. The trend is toward active, or self-healing, AIOps tools.

These tools are all about the data. They store data as they monitor systems and can determine issues that need immediate attention, such as a down storage server. Or, they can deeply analyze historical data to determine trends that may portend a failure or other potential issue. The lifeblood of any AI system is the data needed to train the AI model, and this is the opportunity presented to AIOps tools. Monitored cloud or on-premises systems spin-off gigabytes of data each week, and that data can be fed into analytic systems augmented by AI.

Enterprises that wish to leverage these tools should be careful to understand their capabilities, and should also test the tools across both enterprise cloud and non-cloud platforms. There have been compatibility issues reported, most discovered after deployment.

Many of these tools are moving to an “on-demand” model, meaning that they will offer cloud-based services. This is an opportunity for those that have, or will have, the majority of their systems on public clouds. However, it may not be a good model for those that still have the majority of systems on-premises.

Gartner believes Artificial Intelligence (AI) security will be a top strategic technology trend in 2020, and that enterprises must gain awareness of AI’s impact on the security space. However, many enterprise IT leaders still lack a comprehensive understanding of the technology and what the technology can realistically achieve today. It is important for leaders to question exasperated Marketing claims and over-hyped promises associated with AI so that there is no confusion as to the technology’s defining capabilities.

IT leaders should take a step back and consider if their company and team is at a high enough level of security maturity to adopt advanced technology such as AI successfully. The organization’s business goals and current focuses should align with the capabilities that AI can provide. 

A study conducted by Widmeyer revealed that IT executives in the U.S. believe that AI will significantly change security over the next several years, enabling IT teams to evolve their capabilities as quickly as their adversaries. 

Of course, AI can enhance cybersecurity and increase effectiveness, but it cannot solve every threat and cannot replace live security analysts yet. Today, security teams use modern Machine Learning (ML) in conjunction with automation, to minimize false positives and increase productivity.

As adoption of AI in security continues to increase, it is critical that enterprise IT leaders face the current realities and misconceptions of AI, such as:

Artificial Intelligence as a Silver Bullet

AI is not a solution; it is an enhancement. Many IT decision leaders mistakenly consider AI a silver bullet that can solve all their current IT security challenges without fully understanding how to use the technology and what its limitations are. We have seen AI reduce the complexity of the security analyst’s job by enabling automation, triggering the delivery of cyber incident context, and prioritizing fixes. Yet, security vendors continue to tout further, exasperated AI-enabled capabilities of their solution without being able to point to AI’s specific outcomes.

If Artificial Intelligence is identified as the key, standalone method for protecting an organization from cyberthreats, the overpromise of AI coupled with the inability to clearly identify its accomplishments, can have a very negative impact on the strength of an organization’s security program and on the reputation of the security leader. In this situation, Chief Information Security Officers (CISO) will, unfortunately, realize that AI has limitations and the technology alone is unable to deliver aspired results. 

This is especially concerning given that 48% of enterprises say their budgets for AI in cybersecurity will increase by 29 percent this year, according to Capgemini.

Read more: Improve Your Bottom Line With Contract Automation and AI

Automation Versus Artificial Intelligence

We have seen progress surrounding AI in the security industry, such as the enhanced use of ML technology to recognize behaviors and find security anomalies. In most cases, security technology can now correlate the irregular behavior with threat intelligence and contextual data from other systems. It can also use automated investigative actions to provide an analyst with a strong picture of something being bad or not with minimal human intervention. 

A security leader should consider the types of ML models in use, the biases of those models, the capabilities possible through automation, and if their solution is intelligent enough to build integrations or collect necessary data from non-AI assets. 

AI can handle a bulk of the work of a Security Analyst but not all of it. As a society, we still do not have enough trust in AI to take it to the next level — which would be fully trusting AI to take corrective actions towards those anomalies it identified. Those actions still require human intervention and judgment.

Read more: The Nucleus of Statistical AI: Feature Engineering Practicalities for Machine Learning

Biased Decisions and Human Error

It is important to consider that AI can make bad or wrong decisions. Given that humans themselves create and train the models that achieve AI, it can make biased decisions based on the information it receives.

Models can produce a desired outcome for an attacker, and security teams should prepare for malicious insiders to try to exploit AI biases. Such destructive intent to influence AI’s bias can prove to be extremely damaging, especially in the legal sector. 

By feeding AI false information, bad actors can trick AI to implicate someone of a crime more directly. As an example, just last year, a judge ordered Amazon to turn over Echo recordings in a double murder case. In instances such as these, a hacker has the potential to wrongfully influence ML models and manipulate AI to put an innocent person in prison. In making AI more human, the likelihood of mistakes will increase.

What’s more, IT decision-makers must take into consideration that attackers are utilizing AI and ML as an offensive capability. AI has become an important tool for attackers, and according to Forrester’s Using AI for Evil report, mainstream AI-powered hacking is just a matter of time.

AI can be leveraged for good and for evil, and it is important to understand the technology’s shortcomings and adversarial potential.

The Future of AI in Cybersecurity

Though it is critical to acknowledge AI’s realistic capabilities and its current limitations, it is also important to consider how far AI can take us. Applying AI throughout the threat lifecycle will eventually automate and enhance entire categories of Security Operations Center (SOC) activity. AI has the potential to provide clear visibility into user-based threats and enable increasingly effective detection of real threats.

There are many challenges IT decision-makers face when over-estimating what Artificial Intelligence alone can realistically achieve and how it impacts their security strategies right now. Security leaders must acknowledge these challenges and truths if organizations wish to reap the benefits of AI today and for years to come.

Read more: AI in Cybersecurity: Applications in Various Fields

Source: Aithority.com

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