Here are 9 AI Use Cases Happening in Business Today

Artificial intelligence (AI) is increasingly getting attention from enterprise decision makers. Given that, it’s no surprise that AI use cases are growing. According research conducted by Gartner, smart machines will achieve mainstream adoption by 2021, with 30 percent of large companies using AI. These technologies, which can take the form of cognitive computing, machine learning and deep learning, are now […]

Artificial intelligence (AI) is increasingly getting attention from enterprise decision makers. Given that, it’s no surprise that AI use cases are growing. According research conducted by Gartner, smart machines will achieve mainstream adoption by 2021, with 30 percent of large companies using AI.

These technologies, which can take the form of cognitive computing, machine learning and deep learning, are now tapping advanced capabilities such as image recognition, speech recognition, the use of smart agents, and predictive analytics to reinvent the way organizations do business. Combined with other digital technologies, including the Internet of Things (IoT), a new era of AI promises to transform business.

Here’s a look at 10 leading AI use cases and how organizations can use them to gain a competitive advantage:

Marketing: AI for Real Time Data 

The use of real-time data, Web data, historical purchase data, app use data, unstructured data and geolocation information have introduced the ability to deliver information, product recommendations, coupons and incentives at the right time and place. AI allows companies to engage in personalized marketing and slide the dial closer to one-to-one relationships.

In addition, businesses gain competitive advantage by using machine learning and deep learning for sentiment analysis by analyzing e-mail and social media streams. More advanced systems can detect a person’s mood from photos and videos. This helps systems respond contextually and create more targeted marketing and interactions.

Retail Sales: AI for Voice and Image Search

Artificial intelligence in retail is transforming the way people shop and buy items ranging from clothes to cars. Voice search and image search are now widespread. Amazon and many other retailers now incorporate these tools in their apps. Next generation AI is also taking shape. For example, augmented reality (AR) lets shoppers view a sofa or paint color superimposed in their house or office. Virtual reality (VR) allows consumers to sit inside a vehicle and even test drive it without leaving home. Audi, BMW and others have developed VR systems for shoppers.

But the AI use cases don’t stop there. AI in retail extends to bots and virtual assistants that recommend products and provide information; algorithms that helps sales teams focus on high value customers and high probability transactions; and predictive analytics that factor in weather, the price of raw goods and components, or inventory levels to adjust pricing and promotions dynamically. Clothing retailer North Face, for instance, asks customers a series of questions related to a purchase at its website. Not only does this lead customers to the right product, it taps machine learning to gain insights that potentially lead to higher cart values and additional sales.

Customer Support: AI for Natural Language

AI in retail is emerging as a powerful force, but customer support is also harnessing the technology for competitive advantage. Bots and digital assistants are transforming the way support functions take place. These technologies increasingly rely on natural language processing to identify problems and engage in automated conversations. AI algorithms determine how to direct the conversation or route the call to the right human agent, who has the required information on hand. This helps shorten calls and it produces higher customer satisfaction rates. A Forrester study found that 73 percent of customers said that valuing their time is the most important thing a company can do to provide them with good online customer service.

Manufacturing: AI Powers Smart Robots

Robotics has already changed the face of manufacturing. However, robots are becoming far more intelligent and autonomous, thanks to AI. What is machine learning used for in factories? Many companies are building so-called “smart manufacturing” facilities that use AI to optimize labor, speed production and improve product quality. Companies are also turning to predictive analytics to understand when a piece of equipment is likely to require maintenance, repair or replacement.

For example, Siemens is now equipping gas turbine systems with more than 500 sensors that continuously monitor devices and machines. All this data is helping create the manufacturing facility of the future, sometimes referred to as Industry 4.0. Smart manufacturing–which merges the industrial IoT and AI–is projected to grow from $200 billion in 2018 to $320 billion by 2020, according to a study conducted by market research firm TrendForce.

Read the source article in Datamation.

3 Companies Using AI to Forge New Advances in Healthcare

When you think of artificial intelligence (AI), you might not immediately think of the healthcare sector. However, that would be a mistake. AI has the potential to do everything from predicting readmissions, cutting human error and managing epidemics to assisting surgeons to carry out complex operations. Here we take a closer look at three intriguing […]

When you think of artificial intelligence (AI), you might not immediately think of the healthcare sector.

However, that would be a mistake. AI has the potential to do everything from predicting readmissions, cutting human error and managing epidemics to assisting surgeons to carry out complex operations.

Here we take a closer look at three intriguing stocks using AI to forge new advances in treating and tackling disease. To pinpoint these three stocks, we used TipRanks’ data to scan for ‘Strong Buy’ stocks in the healthcare sector. These are stocks with substantial Street support, based on ratings from the last three months. We then singled out stocks making important headways in AI and machine learning.

BioXcel Therapeutics Inc.

This exciting clinical stage biopharma is certainly unique. BioXcel (BTAI) applies AI and big data technologies to identify the next wave of neuroscience and immuno-oncology medicines. According to BTAI this approach uses “existing approved drugs and/or clinically validated product candidates together with big data and proprietary machine learning algorithms to identify new therapeutic indices.”

The advantage is twofold: “The potential to reduce the cost and time of drug development in diseases with substantial unmet medical need,” says BioXcel. Indeed, we are talking $50 – 100 million of the cost (over $2 billion) typically associated with the development of novel drugs. Right now, BioXcel has several therapies in its pipeline including BXCL501 for prostate and pancreatic cancer. And it seems like the Street approves. The stock has received five buy ratings in the last three months with an average price target of $20.40 (115% upside potential).

“Unlocking efficiency in drug development” is how H.C Wainwright analyst Ram Selvaraju describes Bioxcel’s drug repurposing and repositioning. “The approach BioXcel Therapeutics is taking has been validated in recent years by the advent of several repurposed products that have gone on to become blockbuster franchises (>$1 billion in annual sales).” However, he adds that “we are not currently aware of many other firms that are utilizing a systematic AI-based approach to drug development, and certainly none with the benefit of the prior track record that BioXcel Therapeutics’ parent company, BioXcel Corp., possesses.”

Microsoft Corp.

Software giant Microsoft believes that we will soon live in a world infused with artificial intelligence. This includes healthcare.

According to Eric Horvitz, head of Microsoft Research’s Global Labs, “AI-based applications could improve health outcomes and the quality of life for millions of people in the coming years.” So it’s not surprising that Microsoft is seeking to stay ahead of the curve with its own Healthcare NExT initiative, launched in 2017. The goal of Healthcare NExT is to accelerate healthcare innovation through artificial intelligence and cloud computing. This already encompasses a number of promising solutions, projects and AI accelerators.

Take Project EmpowerMD, a research collaboration with UPMC. The purpose here is to use AI to create a system that listens and learns from what doctors say and do, dramatically reducing the burden of note-taking for physicians. According to Microsoft, “The goal is to allow physicians to spend more face-to-face time with patients, by bringing together many services from Microsoft’s Intelligent Cloud including Custom Speech Services (CSS) and Language Understanding Intelligent Services (LUIS), customized for the medical domain.”

On the other end of the scale, Microsoft is also employing AI for genome mapping (alongside St Jude Children’s Research Hospital) and disease diagnostics. Most notably, Microsoft recently partnered with one of the largest health systems in India, Apollo Hospitals, to create the AI Network for Healthcare. Microsoft explains: “Together, we will be developing and deploying new machine learning models to gauge patient risk for heart disease in hopes of preventing or reversing these life-threatening conditions.”

Globus Medical Inc.

This medical device company is pioneering minimally invasive surgery, including with the assistance of the ExcelsiusGPS robot. Globus Medical describes how the Excelsius manages to combine the benefits of navigation, imagery and robotics into one single technology. And the future possibilities are even more exciting.

According to top Canaccord Genuity analyst Kyle Rose, there are multiple growth opportunities for GMED. He explains: “Currently, ExcelsiusGPS supports the placement of nails and screws in both trauma and spine cases, and we expect Globus to leverage the platform for broader orthopedic indications in future years.” Encouragingly, Rose notes that management has already received positive early feedback and robust demand for the medical robot.

Indeed, in the first quarter Globus reported placing 13 robots vs. Rose’s estimate of just 5 robots. This extra success translated to ~$7.8 million in upside relative to his estimates. On the earnings call, Globus revealed reiterated their long-term vision for ExelsiusGPS as a robotic platform with far more advanced capabilities. This could even include using augmented reality to construct a 3D view of the patient’s external and internal anatomy.

Read the source article in TheStreet.

Here is the Essential Landscape for Enterprise AI Companies

Enterprise companies comprise a $3.4 trillion market worldwide of which an increasingly larger share is being allocated to artificial intelligence technologies. By our definition, “enterprise” technology companies create tools for workplace roles and functions that a large number of businesses use. For example, Salesforce is the primary enterprise software used by sales professionals in a company. Also […]

Enterprise companies comprise a $3.4 trillion market worldwide of which an increasingly larger share is being allocated to artificial intelligence technologies.

By our definition, “enterprise” technology companies create tools for workplace roles and functions that a large number of businesses use. For example, Salesforce is the primary enterprise software used by sales professionals in a company. Also known as a type of customer relationship management software, or CRM, it is the system of record for sales professionals to enter in their contacts, progress of leads, and for sales metrics to be tracked. Any company directly selling their products and services would benefit from a CRM.

Plenty of enterprise companies use combinations of automated data science, machine learning, and modern deep learning approaches for tasks like data preparation, predictive analytics, and process automation. Many are well-established players with deep domain expertise and product functionality. Others are hot new startups applying artificial intelligence to new problems. We cover a mix of both.

To help you identify the best tools for your business, we’ve broken up our landscape of enterprise AI solutions into functional categories to match organizational workflows and use cases. Most of these enterprise companies can be classified in multiple categories, but we focused on the primary value add and differentiation for each company.

BUSINESS INTELLIGENCE (BI)

This function derives intelligence from company data, encompasses the business applications, tools, and workflows that bring together information from all parts of the company to enable smart analysis. From streamlining data preparation like Paxata and Trifacta, to connecting data more effectively from different silos like Tamr and Alation, and even automating reports and generating narratives like Narrative Science and Yseop, enterprise companies are improving BI workflows with artificial intelligence.

PRODUCTIVITY

Productivity at work is often stunted by a myriad of tiny tasks that consume your attention, i.e. “death by a thousand cuts.” Many productivity tools have emerged to eliminate such tasks, such as the endless back and forth required to schedule meetings. Luckily, many of these productivity tools are virtual scheduling assistants like X.ai, FreeBusy, and Clara Labs.

CUSTOMER MANAGEMENT

Taking care of your customers is no easy task. Enterprise companies have recognized this critical area as ripe for disruption with artificial intelligence. DigitalGenius utilizes AI to sift through your customer service data and automate customer service operations. Inbenta’s AI-powered natural language search enables delivery of self-service support in forums and virtual agents. Luminoso creates visual representations of customer feedback, allowing companies to better understand what consumers want.

HR & TALENT

With the average tenure of a hire getting shorter, hiring and talent management is arguably one of the most difficult areas for every company to tackle. Where can you find the right candidates and how do you keep hires engaged? Companies like Entelo and Scout work from the top of the funnel to get you the most qualified candidates while others like hiQ Labs utilize public data to warn you of staff attrition risks and enable you to create retention strategies.

B2B SALES & MARKETING

No one likes to waste time tediously doing data entry or spend hours sometimes googling and sifting through LinkedIn trying to get that marginal bit of information on a lead. Perhaps that’s why professionals in these functions are willing to embrace and experiment with new tools. Some automate data entry and improve forecasting accuracy like Fusemachines and the AI-powered sales assistant Tact, while others like Lattice Engines and Mintigo utilize thousands of data sources to surface the most qualified prospects and opportunities. You also have Salesforce’s Einstein who has the intention of bringing AI and automation throughout the entire sales ecosystem.

CONSUMER MARKETING

So much data and intelligence can be gathered about your consumers through social channels, distribution channels, media channels, etc. Smart tools can not only crawl through this data, but analyze and understand what’s being said or done. Lexalytics is a text analytics platform that translates billions of unstructured data pieces and online signals into actionable insights for the company. Affinio uses deep learning to surface social fingerprints for the brand by creating interest-based clusters. Brands now have a better understanding of their customer segments, behaviors, and sentiments.

FINANCE & OPERATIONS

Finance & operations includes the back office, forecasting, accounting, and operational roles required to run a company. Since nobody likes paperwork, this area is ripe for automation. HyperScience recently came out of stealth with their $18 million Series A in December of 2016 to completely automate back office operations like form processing and data extraction through AI. Another company called AppZen is an automated audit platform that can instantly detect fraud and compliance issues, freeing up T&E teams from tedious manual audits and checks. The tools in this space reap immediate returns for companies due to the volume and repetitive nature of some of the tasks.

Read the source article at TopBots.

Artificial Emotional Intelligence and Emotion AI at Work for Major Brands

Imagine a world in which machines interpret the emotional state of humans and adapt their behavior to give appropriate responses to those emotions. Well, artificial emotional intelligence, which is also known as emotion AI or affective computing, is already being used to develop systems and products that can recognize, interpret, process, and simulate human affects […]

Imagine a world in which machines interpret the emotional state of humans and adapt their behavior to give appropriate responses to those emotions.

Well, artificial emotional intelligence, which is also known as emotion AI or affective computing, is already being used to develop systems and products that can recognize, interpret, process, and simulate human affects (with an “a,” not an “e”). In psychology, an “affect” is a term used to describe the experience of feeling or emotion.

If you’ve seen “Solo: A Star Wars Story”, then you’ve seen the poster child for artificial emotional intelligence: L3-37.

Lando Calrissian’s droid companion and navigator (voiced by Phoebe Waller-Bridge) instigates a slave revolt to escape from Kessel, but is severely damaged during the diversion. Lando (played by Donald Glover) is also injured during the getaway.

The “woke robot” demonstrates the ability to simulate empathy by interpreting the emotional state of a human, adapting its behavior to him, and giving an appropriate response to those emotions.

Now, this example might lead some video marketers and advertisers to think that emotion AI is science fiction. But, it is very real.

A number of companies are already working to give computers the capacity to read our feelings and react, in ways that have come to seem startlingly human. This includes Affectiva, an emotion measurement technology company that spun out of MIT’s Media Lab in 2009, and Realeyes, an emotion tech company that spun out of Oxford University in 2007.

So, how do their technologies help brands, agencies, and media companies improve their advertising and marketing messages? Let’s tackle this question by examining how affective computing works.

How Does Artificial Emotion Intelligence Work?

Brands know emotions influence consumer behavior and decision making. So, they’re willing to spend money on market research to understand consumer emotional engagement with their brand content.

Affectiva uses a webcam to track a user’s smirks, smiles, frowns, and furrows, which measure the user’s levels of surprise, amusement, or confusion.

It also uses a webcam to measure a person’s heart rate without wearing a sensor by tracking color changes in the person’s face, which pulses each time the heart beats.

Affectiva has turned this technology into a cloud-based solution that utilizes “facial coding” and emotion recognition software to provide insight into a consumer’s emotional responses to digital content. All a brand or media company needs are some panelists with standard webcams and internet connectivity.

As viewers watch a video, Affectiva’s product, Affdex for Market Research, measures their moment-by-moment facial expressions of emotions. The results are then aggregated and displayed in a dashboard.

Affdex for Market Research also provides video marketers and advertisers with norms that leverage Affectiva’s extensive emotion database and tie directly to outcomes such as brand recall, sales lift, purchase intent, and likelihood to share.

These norms benchmark a video or ad against ones from competitors – by geography, product category, media length, and repeat views. About one-third of the Fortune Global 100, including brands such as Kellogg’s and Mars as well as media companies like CBS, have used Affdex for Market Research to optimize their content and media spend.

By comparison, Realeyes uses webcams as well as computer vision and machine learning to measure how people feel as they watch video content online.

First, a brand, agency, or media company selects a specific geography and audience segment that it wants to test.

Next, the Realeyes system provides 300 target viewers, who watch videos on their own device anytime they choose.

Then, the system’s algorithms process and analyze facial expressions in the cloud and show results on a dashboard within 24 hours.

The reports provided by Realeyes combine both creative testing and media planning insights to enable video marketers and advertisers to understand how consumers feel about their video content.

This enables brands (such as Coca-Cola, Hershey’s, and Mars), agencies (like Ipsos, MarketCast, and Publicis), as well as media companies (such as Oath, Teads, and Turner) to optimize their content and target their videos at the right audiences.

Read the source article at SearchEngineJournal.com.

Giving Artificial Intelligence a Little Common Sense

By Mark Barry of Luminoso As we all go about our day using the internet, talking to brands, and interacting online, we don’t adhere strictly to the writing guidelines of the Associated Press. We use a lot of slang, jargon, technical corporate terms, figurative language, and other communication shorthands — unstructured digital data that other humans understand easily, […]

By Mark Barry of Luminoso

As we all go about our day using the internet, talking to brands, and interacting online, we don’t adhere strictly to the writing guidelines of the Associated Press. We use a lot of slang, jargon, technical corporate terms, figurative language, and other communication shorthands — unstructured digital data that other humans understand easily, but that confounds Artificial Intelligence (AI).

The problem is that we tend to use very colorful, interesting, and funny language because we know that our words could reach a larger audience that way. As linguistic philosopher Paul Grice suggested, “we are concise in our language, leaving out things we expect everyone to know because we don’t want to be considered boring.” This results in data that is very unpredictable, disorganized, and constantly evolving, ultimately leaving AI in the dark because it lacks a little something called common sense.

This glue (common sense) that holds language together makes understanding it  uniquely difficult for AI systems because it is often left out of the datasets. To get there, we need to provide AI with a very deep, intuitive understanding of the language we use, along with the nuances and expressions that come along with it.

As linguist George Lakoff suggests, we understand complicated concepts by making analogies to simpler ideas. For example, in English, if we’re talking about data, we often use words for water, like information flow, as a way of conveying a core concept.

For instance, if you and I were having a conversation, I would never feel the need to tell you that if I drop something, it falls down. I expect you to know that. I would also expect you to know that when you get your morning coffee, it comes in a mug. These unspoken details are what AI systems need to make the right connections and contextualize our conversations.

Understanding Customers

When businesses started trying to understand digital customer feedback, they began with sentiment analysis (the ability to tell whether or not a statement is negative or positive). But the ceiling was hit pretty quickly when they realized that sentiment analysis misses out on the important stuff. It doesn’t truly capture why something is positive or negative, how to address it, or which aspects are more positive than others, let alone whether you’re even asking the right questions in the first place.

Over time, AI became smarter thanks to various deep learning techniques, but a different problem emerged: lack of data. Case in point; when Google’s DeepMind AI began learning to beat human players in the game of Go , it was crunching data on three million Go games. But that wasn’t enough. Once they ran out of data, they had it start playing itself. This is problematic though, because to teach an AI how to understand what customers are saying, you can’t exactly have it talk to itself to generate new training data.

It became clear that in order to build an AI that could learn how to understand customer feedback and add value to the conversation, we need to provide more sources of knowledge about how the world works, beyond the limited data from customer communications.

Developing Common Sense AI

ConceptNet is a large, open source knowledge base created at the MIT media lab in 2006. The knowledge base represent concepts, not just words, so that relations can be made and understood as a graph.

At Luminoso, our approach to embedding common sense into our products is to take background knowledge from ConceptNet and pair it with machine learning. This allows the system to adapt to new datasets without the need for heavy resources or lots of training data.

Meaning, the system strives to offer the kind of machine learning and natural language understanding you hear everyone talking about without the need for human intervention.

Luminoso uses a very small amount of data — the kind of data you already have — so you can start applying it to all sorts of unstructured text and start extracting insights to disseminate throughout the organization.

To demonstrate the value of common sense AI out in the wild, here’s a quick use-case: Luminoso was working with a Japanese car manufacturer not long ago. We were looking at feedback from dealerships to help unearth issues they hadn’t yet addressed but knew customers were talking about.

These customers were coming into the dealerships talking about how their cars smelled bad, and they used a lot of different words to describe this smell. Some described it as a wet dog even though they didn’t have a dog, some thought it smelled like an attic, and others simply said it smelled funky. And all of this feedback was being categorized by customer service agents as other, which is that bucket in your call center where unanticipated things that are truly going wrong go to haunt you.

Luminoso investigated this other bucket and found that these different descriptors for a bad odor were related to reports of dew or condensation inside certain models. The manufacturer was able to look into these models and found that the air conditioning hose was coming undone, causing water to leak into the car, leading to condensation, mold, and lo and behold, a bad smell.

Being able to tell dealerships how to address customers who come in with this issue was a huge win for their overall customer satisfaction, and it allowed them to unearth similar insights that may have eluded them going forward.

Ultimately, with the right common sense foundation in natural language understanding, these “lost in translation” moments become easy to reveal along with other “unknown unknowns” that companies spend years trying to get to the bottom of. So whether your goal is to improve customer or employee satisfaction, you’ll need a little common sense to really hear what they’re saying.

For more information, go to Luminoso.

 

Alibaba Furthers Use of AI to Power the Future of Business

Alibaba Group is powering ahead with a range of AI research and initiatives in a bid to realise its vision: To make it easy to do business everywhere and anywhere. That’s according to an Alibaba Group chief scientist and associate dean of machine intelligence and technology, Xiaofeng Ren, who spoke at CeBIT about how to […]

Alibaba Group is powering ahead with a range of AI research and initiatives in a bid to realise its vision: To make it easy to do business everywhere and anywhere.

That’s according to an Alibaba Group chief scientist and associate dean of machine intelligence and technology, Xiaofeng Ren, who spoke at CeBIT about how to develop AI applications that power the future of business.

“Alibaba has changed the everyday life of the Chinese in China. Looking forward, our visionary leader, Jack Ma, wants us to be able to reach two billion consumers and to help 10 million businesses around the world. That’s a very big call, but we already have half of the platforms in place.”

He explained how the company’s enabling services including Ant Financial (mobile online payment platform); Alibaba Cloud (public cloud service); cainiao (logistics branch); and alimama.com (an online marketing and trading platform) are making the vision a reality.

With technology the one universal driver behind all of these things, Ren said AI is a big part of the strategic play. He noted the world is in the third or fourth wave of technological development, and AI fits right into the equation, promising to change both business and society on a massive scale.

“There’s a blending of the digital world and the physical world where we have IoT, robotics, 3D printing, nanotechnologies – there are a lot of things that are happening that could further power the growth of the fourth wave. And I certainly think that artificial intelligence will be a big part of this revolution,” he said.

Ren’s machine intelligence technology division spearheads Alibaba’s efforts into AI, and aims to bring sweeping and disruptive changes both to the online player and to China’s dynamic business landscape. The independent R&D division is working across the Alibaba group in areas including speech recognition, computer vision, natural language understanding and optimisation and learning.

Projects underway

Ren detailed how emerging technologies are being developed at Alibaba, and how they will impact its future and the global retail landscape and enhance customer experience. One example of business innovation comes in the form of AliMe, a chatbot Alibaba developed that understands what people say both in terms of the text and the speech.

“It can respond to you in a number of ways. It can be a shopping assistant. For example, if you want to buy a train ticket, it can help you with it. It can be a customer service. It can help you with some of the problems and provide information to you. It can also be a generic ‘chatty cathy,’” he said, explaining some people just want to chat if they are bored,” he explained.

“Last year, this system handled about 95 per cent of requests – 9 million requests – and people were really happy with how this system performed in the real-world as a stress test.”

Another example is the work the company has done over the past four years around ‘Image Search’, which is being used by Taobao, the Chinese online shopping website, headquartered in Hangzhou, China, and a subsidiary of Alibaba Group. Taobao is considered one of the world’s biggest e-commerce websites, as well as one of the world’s top 10 most visited websites according to Alexa.

“On the Taobao app, when you want to search for a product, you can actually take a picture and upload a picture and it will find a similar picture in the catalogue,” Ren said. “There are certain limits on how you can describe a product. But on the other hand, a picture is worth a thousand words. One you put in a picture, it becomes much more clear what you’re looking for.”

He said 14 billion people use this feature everyday, and the company has three billion images of over 10 million products. “Once we have a technology and we get it to the mature point, we actually make it available for everyone to use.”

Image Search – which is now mature enough and announced as part of the Alibaba Cloud offering in March  – was recently picked up by The Iconic, an Australia online fashion and footwear store.

“Today you can actually use the search functionality in Iconic. It works in a similar way, you can take pictures, upload the pictures, and find similar products,” Ren said. “I’m excited to see in terms of the work of Image Search, not just into one product, or one part of the world, but useful to a lot more people and a lot more businesses.”

Other projects come in the area of media and video in terms of copyright protection and content, as well as indexing and search. Alibaba launched Whale Watching last year, a unified platform for protecting and trading video content.

Read the source article at CMO.

AI and Business Strategy: Think Big, Start Small and Scale Fast

It’s time to take a step back from the noise and hype surrounding artificial intelligence (AI). Businesses have been inundated with AI sales pitches promoting the technology’s potential to automate tasks, increase speed and accuracy and cut costs. But what’s the long-term plan? Most businesses lack a vision of how AI will transform their operations. Developing […]

It’s time to take a step back from the noise and hype surrounding artificial intelligence (AI). Businesses have been inundated with AI sales pitches promoting the technology’s potential to automate tasks, increase speed and accuracy and cut costs. But what’s the long-term plan? Most businesses lack a vision of how AI will transform their operations.

Developing an overarching AI strategy

Rather than the piecemeal adoption of AI systems, some believe businesses need to develop an overarching strategy for how to embed AI in their organisation over time.

“The most important thing is having a comprehensive and holistic view of AI sourcing within the organisation,” says Mohammed Chaara, a former Lenovo strategist who is now “an evangelist” for AI. Working out what kind of AI is needed for different processes and whether these will be carried out in-house, outsourced or in partnership is an important step to developing a strategy, he says.

Mr. Chaara believes businesses need to base their AI strategy on four areas. Low-risk, low-value AI involves automating low-level repetitive tasks, such as data processing, which can be outsourced.

Low-value, high-risk AI includes audience-targeting driven by machine-learning, which can cut the costs of identifying and reaching audiences, and boost profits by targeting the right customers. But this risks wasting the marketing budget, if the targeting is wrong, and so is high risk. It should be developed in tandem with external partners, he says.

The third area is high-value, low-risk tasks, such as the credit scoring models in financial services. This offers high value, but is low risk and so complex no one else can decipher it.

Finally, there is high-risk and high-value activity, the uncharted territory where there is no solution on the market, for instance using AI systems to fuel new product development.

The last two categories could involve building in-house solutions or buying in the expertise through acquisitions.

If you can adopt these four frameworks, you can have an idea of when you need to migrate AI from one point to another,” says Mr Chaara. “Without having that comprehensive view, the organisation will not be able to move quickly and in an agile way from one category to another.”

The key to a successful AI strategy: think big, start small and scale fast

Some doubt that businesses have the time to develop in-depth strategies for AI and are best off implementing it as needed. Dr Lee Howells, an automation and AI expert at PA Consulting Group, says companies should use AI to solve a specific business problem rather than being led by the technology. He fears an AI strategy could be outdated before it gets implemented as developments are moving so fast.

“When organisations try and introduce AI, they need to find a balance between their strategic ambition for AI and getting started quickly. You can never have a strategy that is 100 per cent correct, and with AI people deliberate about it and the greatest problem is inertia; they don’t want to get started,” he says, adding that the best approach is to “think big, start small and scale fast”.

AI goals should be long-term and realistic

One of the fastest growing areas of AI is robotic process automation (RPA), which uses software to automate repetitive tasks, such as replying to standard emails, processing customer orders and updating payrolls.

Guy Kirkwood, chief evangelist at RPA company UiPath, says: “When companies say they want AI, what they actually mean is they want the tools necessary to automate more of their processes, because that is what the real strength of AI is.”

But he adds: “RPA is a tool not a panacea.” Mr Kirkwood believes organisations are adopting RPA so quickly because it cuts across all geographies, industries and services. “There is no limit to where it can spread,” he says. “The addressable market for RPA is unlimited. It is one robot per employee everywhere – that’s the potential, which is pretty staggering.”

Read the source article at Raconteur.

These 4 Apps Powered by AI Will Strengthen Your Business

Leaders know they need technology to advance their businesses and boost their teams — but many of them are scared to use it. They’re not alone; Americans as a whole fear technology more than death. Christopher Bader, a sociology professor at Chapman University and one of the authors of the study that found Americans find robots more terrifying […]

Leaders know they need technology to advance their businesses and boost their teams — but many of them are scared to use it. They’re not alone; Americans as a whole fear technology more than death.

Christopher Bader, a sociology professor at Chapman University and one of the authors of the study that found Americans find robots more terrifying than dying, said, “People tend to express the highest level of fear for things they’re dependent on but that they don’t have any control over, and that’s almost a perfect definition of technology.”

The great thing is that leaders don’t necessarily need to know how the technology works to benefit from it. Take these four platforms, which are powered by artificial intelligence but do the work for businesses:

1. Spiro.ai

Spiro.ai is an AI-powered CRM that empowers salespeople to nurture leads and close sales without the distraction of data entry. Spiro uses AI to proactively build a to-do list for sales reps and is the only CRM with a built-in email assistant. The email assistant provides sales reps and managers all the information they need, without requiring them to log in to their CRM.

The biggest selling point for the tech-averse may be that the CRM relies on an algorithm built by salespeople for salespeople, with the aim of creating a CRM people will actually use.

Because the CRM cuts down on the need to organize between calls, it fuels sales productivity and pushes salespeople to follow up in the quick timeline needed to close sales. And it offers another tech advantage in the form of reporting: Because Spiro self-populates with data, the reports it sends to managers are eight times richer with data than many competing platforms’ reports.

2. Pi

Pi is an AI-powered social marketing tool to help companies boost their social strategies by analyzing followers’ interactions. After connecting with a company’s Twitter or Facebook account, Pi evaluates followers’ profiles, posts, and comments (through natural language processing and sentiment analysis) to develop a list of relevant topics and tones. The app can recommend when to publish an associated post and use performance to continually assess and adjust its suggestions.

For those who don’t have the time to look at others’ accounts, the AI’s biggest benefit is that it can create a pool of links to consider posting, and it can predict how well a drafted post will perform once posted. It also offers a marketplace for sponsored posts.

3. Legal Robot

Legal issues are perhaps as terrifying for some as technology itself; enter Legal Robot, an AI-powered “legal advisor” that helps both lawyers and consumers build contracts. Built to overcome the difficulty of understanding legal language, the app uses deep learning and natural language processing to create models of contracts for various scenarios and uses. It can then translate the terminology into layman’s terms, compare documents to create a language benchmark for consistency, and ensure compliance.

The app aims to help businesses identify risks and pinpoint their specific blind spots in creating contracts, and its ability to learn and transform its understanding boosts its likelihood of doing that.

4. Learn Chinese

Microsoft’s Learn Chinese may specifically aim to teach users how to speak Chinese, but the AI-powered language app is a precursor of things to come. The app utilizes deep neural networks to use speech to help people learn to actually speak Chinese, not simply learn its grammar rules.

Learn Chinese uses speech clues to anticipate what a learner wants to say and then scores the speaker’s attempt compared to native speakers and synthesized examples. This allows users to practice language skills they may need to use on business trips or with visiting companies without face-to-face teacher availability.

The AI shines when users consider that people can learn either language featured– Chinese or English–through the app. Learn Chinese also identifies individual words needing more practice and offers audio samples of how the words should be pronounced for future reference, saving business leaders everywhere from the risk of dying of embarrassment.

Being dependent upon something without being comfortable with it is a scary prospect. Spending more time with AI-powered tools, however, can not only make leaders less scared of technology, but it can also improve their business–and the possibility of a stronger company should outweigh any fear.

Read the source article at Inc.com.

Adobe’s CTO Leads Company’s AI Business Strategy

By Ron Miller, TechCrunch Reporter There isn’t a software company out there worth its salt that doesn’t have some kind of artificial intelligence initiative in progress right now. These organizations understand that AI is going to be a game-changer, even if they might not have a full understanding of how that’s going to work just […]

By Ron Miller, TechCrunch Reporter

There isn’t a software company out there worth its salt that doesn’t have some kind of artificial intelligence initiative in progress right now. These organizations understand that AI is going to be a game-changer, even if they might not have a full understanding of how that’s going to work just yet.

In March at the Adobe Summit, I sat down with Adobe executive vice president and CTO Abhay Parasnis, and talked about a range of subjects with him including the company’s goal to build a cloud platform for the next decade — and how AI is a big part of that.

Parasnis told me that he has a broad set of responsibilities starting with the typical CTO role of setting the tone for the company’s technology strategy, but it doesn’t stop there by any means. He also is in charge of operational execution for the core cloud platform and all the engineering building out the platform — including AI and Sensei. That includes managing a multi-thousand person engineering team. Finally, he’s in charge of all the digital infrastructure and the IT organization — just a bit on his plate.

Ten years down the road

The company’s transition from selling boxed software to a subscription-based cloud company began in 2013, long before Parasnis came on board. It has been a highly successful one, but Adobe knew it would take more than simply shedding boxed software to survive long-term. When Parasnis arrived, the next step was to rearchitect the base platform in a way that was flexible enough to last for at least a decade — yes, a decade.

“When we first started thinking about the next generation platform, we had to think about what do we want to build for. It’s a massive lift and we have to architect to last a decade,” he said. There’s a huge challenge because so much can change over time, especially right now when technology is shifting so rapidly.

That meant that they had to build in flexibility to allow for these kinds of changes over time, maybe even ones they can’t anticipate just yet. The company certainly sees immersive technology like AR and VR, as well as voice as something they need to start thinking about as a future bet — and their base platform had to be adaptable enough to support that.

Making Sensei of it all

But Adobe also needed to get its ducks in a row around AI. That’s why around 18 months ago, the company made another strategic decision to develop AI as a core part of the new  platform. They saw a lot of companies looking at a more general AI for developers, but they had a different vision, one tightly focussed on Adobe’s core functionality. Parasnis sees this as the key part of the company’s cloud platform strategy. “AI will be the single most transformational force in technology,” he said, adding that Sensei is by far the thing he is spending the most time on.”

The company began thinking about the new cloud platform with the larger artificial intelligence goal in mind, building AI-fueled algorithms to handle core platform functionality. Once they refined them for use in-house, the next step was to open up these algorithms to third-party developers to build their own applications using Adobe’s AI tools.

It’s actually a classic software platform play, whether the service involves AI or not. Every cloud company from Box to Salesforce has been exposing their services for years, letting developers take advantage of their expertise so they can concentrate on their core knowledge. They don’t have to worry about building something like storage or security from scratch because they can grab those features from a platform that has built-in expertise  and provides a way to easily incorporate it into applications.

The difference here is that it involves Adobe’s core functions, so it may be intelligent auto cropping and smart tagging in Adobe Experience Manager or AI-fueled visual stock search in Creative Cloud. These are features that are essential to the Adobe software experience, which the company is packaging as an API and delivering to developers to use in their own software.

Whether or not Sensei can be the technology that drives the Adobe cloud platform for the next 10 years, Parasnis and the company at large are very much committed to that vision. We should see more announcements from Adobe in the coming months and years as they build more AI-powered algorithms into the platform and expose them to developers for use in their own software.

Parasnis certainly recognizes this as an ongoing process. “We still have a lot of work to do, but we are off in an extremely good architectural direction, and AI will be a crucial part,” he said.

Read the source poast at TechCrunch. 

Corporations Face Off with Hackers Around AI Cybersecurity

The mantra of modern technology is to improve and innovate continuously. It makes sense as we strive to look for more improved ways to get processes, actions and activities done. Automation and machine learning, for instance, is currently used across many industries to streamline basic processes and remove the repetition from a normal worker’s routine. […]

The mantra of modern technology is to improve and innovate continuously. It makes sense as we strive to look for more improved ways to get processes, actions and activities done.

Automation and machine learning, for instance, is currently used across many industries to streamline basic processes and remove the repetition from a normal worker’s routine. Not to mention, machines tend to be more efficient and less resource intensive. A robotic or automated system continues to work at its set performance, never tiring, growing hungry or getting burnt out.

As we create more innovative solutions as a society, do we also set ourselves up for harder, more damaging falls? In regards to AI, for example, do we open up the gates to potentially more dangerous and common attacks?

It’s no secret that the technology at our disposal can be used for both good and bad, it just depends on who has control and possession of the necessary systems. With AI, who is truly in control? Is it possible that hackers and unscrupulous parties may take advantage to create more havoc and trouble for the rest of us?

Does modern AI pose a cybersecurity risk?
A recent study, comprised of 25 technical and public policy researchers from Cambridge, Oxford and Yale, alongside privacy and military experts, reveals a potential risk for misuse of AI by rogue states, criminals and other unscrupulous parties. A list of potential threats would come with digital, physical and political ramifications depending on how the systems and tools were leveraged, used and structured.

The study specifically focuses on plausible and reality-based developments that can or may happen over the next five years. Instead of a “what if” scenario, the idea is more of a “when” over the course of the coming decade.

There’s no reason to be alarmed just yet: the paper doesn’t explicitly say AI is dangerous or will definitely be used to harm modern society, only that there’s a series of risks evident.

In fact, one researcher from Oxford’s Future of Humanity Institute named Miles Brundage said: “We all agree there are a lot of positive applications of AI.”

He also goes on to state that “there was a gap in the literature around the issue of malicious use.” It’s not quite so dire, but instead should serve as a warning. If we intend to use AI more openly in the future, which we certainly do, then we need to come up with more advanced security and privacy measures to protect organizations, citizens and devices.

With self-driving vehicles — controlled primarily by a computer-based AI — it’s possible for hackers to gain access to said vehicles in motion, and take control. By no stretch of the imagination, they could easily careen vehicles off the road, disengage locks and various features, or much worse.

Imagine commercial and military drones being turned into remote-access weapons used by shadow parties and criminals?

These are, of course, worst-case scenarios that will only happen if the necessary administrators and developers don’t spend as much time building robust security and protections into the foundation of these devices.

Read the source article at InfoSecurity Magazine.