Artificial intelligence (AI) and machine learning are buzzwords that have entered the vernacular at many enterprises, but few have managed to realize the full benefits of the technologies. But 2018 may be the year that companies begin more strategic implementations and start realizing some of AI’s benefits.
“The percolation of AI and machine learning technologies into businesses still seems to be in its early stages, ranging over awareness that they need to collect data, to awareness that they already have a lot of data but are not making productive use of it, to rudimentary analyses of these data,” said Pradeep Ravikumar, Associate Professor, Machine Learning Department, School of Computer Science, Carnegie Mellon University.
AI will continue to be a fast-moving field in the coming year, and it’s critical for companies to have close contact and collaborations with those in the AI research community to stay on the cutting edge, Ravikumar said.
“From autonomous drones to AI-powered medical diagnostics, 2018 will see the needs of AI expand beyond research as companies bring these solutions to market,” said Julie Choi, head of marketing in the Artificial Intelligence Products Group at Intel. AI hardware will also need to adapt to new form factors, including low-power chips to support small smart home devices or drones, and more purpose-built hardware to speed the training process in data centers, Choi said.
Here are 10 predictions for how AI will grow and the challenges it will face in the enterprise this year.
1. More AI professionals will be hired
In efforts to realize the benefits of AI, companies will hire a variety of professionals to contribute, according to Alex Jaimes, head of R&D at DigitalOcean. Larger organizations may look to add a Chief AI Officer or other senior-level position who will guide how AI and machine learning can be integrated into the company’s existing products and strategy. Others may look at hire…Continue Reading
Those worried that the rise of artificial intelligence means that robots will take their job might feel comforted by the fact that many AI tools are actually being designed not to replace humans, but to help them do their jobs better.
Though the field is still in its infancy, many young startups came to Europe’s largest tech conference, Web Summit, last week to showcase how their AI tools are working to make people more efficient and productive, in both their personal and professional lives. Here are a few that stood out.
(HONESTLY) TRACKING YOUR TIME
Does it feel like you’ve never got enough time for the things you really want to do in life? Paris-based AI startup Smarter Time is helping its 80,000 users find the time that always seems to be missing by tracking their habits and providing feedback.
[Photo: courtesy of SmarterTime]
The concept is that even if one were to try and track every moment of their day in order to manually analyze and optimize their time, few would bother putting in the really small things, but that’s where much of our time gets lost.“You will think it’s so small, I don’t need to input it, but that’s one way of cheating with your own schedule, because these small bits of time add up,” said Smarter Time’s cofounder and CMO, Anna Winterstein. “If you spend five minutes on Facebook 10 times a day that’s 50 minutes, that’s a huge amount of time, and you get distracted, and studies say you need at least 15 minutes to get focused again.”
Using a plugin that tracks desktop browsing, along with an app that tracks phone usage patterns, Smarter Time seeks to “give its users back time for what really matters,” said Winterstein, adding that the company is building features that will eventually encourage healthy lifestyle habits as well.
“By using the latest scientific research we’re trying to give people advice, like maybe you haven’t been sleeping enough or should be doing more fitness,” she said.
The app is currently available as a free download on Android devices, with additional features available for purchase, and an iOS version coming soon.
CRAFTING THE PERFECT COLD EMAIL
Founded in 2016 and officially launched last week at Web Summit, French startup AiZimov seeks to take the guesswork out of cold emails and solicitations, gathering data and information in order to autonomously craft emails that are more likely to receive a positive response.
[Photo: courtesy of Aizimov]
“All you have to put in are four things; first name, last name, email, and company,” said AiZimov’s CEO, Jérôme Devosse. With that information Devosse says the tool crawls the internet for every mention of the person and their organization, and crafts a message tailored to them. That could include references to their latest position paper, their company’s latest press release, their personal Twitter feed, or the hobbies listed on their LinkedIn profile.“If the guy, for example, has done a marathon, I may finish the email by saying ‘by the way, I also did a marathon in Rome, here is my time, how do we compare?’ to get their attention,” said Devosse.
Over time the tool collects information on the sorts of emails that get the best responses among specific target audiences and adjusts various factors–such as tone, length, content, and the time it’s sent–accordingly. While the program optimizes a first draft, the sender still has the opportunity to tweak the email according to their preferences and style, which AiZimov gradually learns to replicate.
“The tool will learn how people in that industry and that country react to different propositions; do they like humor? Do they like a formal tone? Do they like if we talk about their expertise?” explains Devosse.
Though a limited number of users can still get a free trial, Devosse says that it will eventually come with a price tag. As a result the company is targeting B2B companies to be used by their sales departments.
MANAGING AND IMPROVING YOUR WEBSITE
Those who manage their own website typically have three options for improving and optimizing its effectiveness: relying on free tools that require an understanding of website analytics, hiring a firm to help with website optimization, or doing nothing. With little time or resources to dedicate, many freelancers and startups must opt for the latter.
Based at the Technical University of Copenhagen, Canecto hopes to spread website optimization to the masses, providing everyone with the ability to affordably improve the performance of their online presence. “We remove the analytical process and just tell them exactly what to do to improve their website,” said Canecto’s CEO, Per Damgaard Husted, explaining that it doesn’t require significant resources or technological proficiency.
[Photo: courtesy of Canecto]
The tool, which officially launched to the public at Web Summit, seeks to help its users increase their visitors’ time onsite, and will eventually be able to optimize for other metrics as well. “What we’re working on is to enable you to put in your business goals, and get recommendations,” he said. “So in a couple of months you can put in say conversion goals, signups to the newsletter, downloading a PDF file, whatever generates value for you.”By downloading the Canecto script for their content management platform, users are not only able to get detailed information about how visitors interact with their website, but recommendations on how to improve. Such recommendations can range from color scheme to the prominence of photos to the length, tone, and content of text to the optimal number of links, videos, and images.
The tool even tracks social media to help provide additional recommendations based on real-time trends and interests amongst the target audience.
“It will tell you what are the interests of the people who have downloaded that PDF file or signed up for the newsletter, and you can see the reverse, the people who didn’t, and see the difference,” said Damgaard Husted.
Damgaard Husted adds that Canecto’s basic features, which are targeted toward individual freelancers and small business owners, are now available for free, while its more advanced tools, which can help optimize media spending, are available for a cost.
YOUR OWN EXECUTIVE ASSISTANT
The decline in executive assistant roles following the Great Recession has always confused Roy Pereira. Though originally considered a cost-saving measure, the extra time now spent by executives on organizing schedules, preparing meeting notes, and planning travel logistics often proves more expensive. “I wanted to have my own EA, so I decided to build it,” said the Toronto-based entrepreneur.
The resulting product, Zoom.AI, can learn your habits and preferences, make recommendations, schedule meetings automatically and order a car to take you there.
[Photo: courtesy of ZoomAI]
“One of our most important tasks is to get you prepared for the meeting,” said Pereira. “So when you’re in the Uber you’ll pull up two pages of information on the person you’re meeting with, based on public information.”That information can include work history, common connections and friends on social media, recent media appearances, research papers, blog posts, common interests, and an analysis of personality traits.
“There’s also informational discovery inside the office,” said Pereira, who explains that the application can pull information from public documents, internal customer relations management systems, digital files, and other support systems. “With a couple of requests you can just ask, ‘where is the N.D.A. for Coca-Cola that we did last month?’ and it will find it,” he adds.
Asking Zoom.AI for the location of a file or to schedule a meeting is as simple as sending a message via text, Slack, Facebook, Skype, or any of the 16 compatible chat platforms. “It’s like talking to a person in a direct message,” said Pereira.
For example, if Pereira sends a message to his Zoom.AI assistant asking to schedule a coffee with a friend, the program will find time in both schedules near the time of day and for the duration that he typically takes a coffee break, at the coffee shop he frequents that is most geographically convenient for both participants, and send a meeting request.
According to Pereira this technology gives users an average productivity boost of 14%, or 25 hours a week.
Over the past year or so, earbuds with translation tech have been popping up everywhere, signaling the evolution of an industry. Headphones are now capable of being more than just a means to deliver music — if the tech is good enough, they can act as a bridge between disparate cultures, bringing people together to foster mutual understandings.
The new Bluetooth-enabled Mars wireless earbuds, a collaborative project from Line Corporation and Naver Corporation (a leading internet provider in Korea and Line’s parent company), aim to do just that. Boasting real-time ear-to-ear translation of 10 different languages, Mars is unique in that it is designed for each person to wear one earbud (as opposed to needing two pairs). The earbuds were named a CES 2018 Best of Innovation Honoree at CES Unveiled New York on Thursday, November 9.
Scheduled for release in early 2018, Mars support Line’s Clova artificial intelligence, a virtual assistant which takes cues from Siri, Alexa, and Google Assistant. Aside from translation, Clova can help users stream music from several sources, check the weather forecast, and control Internet of Things (IoT) devices, all via voice commands. Line touts Clova as the first A.I. platform developed specifically with Asian markets in mind; Clova integration will be available at launch in Korea and roll out to other markets over time, though we don’t have any sort of timetable.
Microphones inside the Mars — Line doesn’t specify but we assume they’re bone-conduction mics — feature automatic ambient noise blocking, ensuring that users can take phone calls comfortably, even in loud, busy environments. For translation purposes, supported languages (for now) include: English, Korean, Mandarin, Japanese, Spanish, French, Vietnamese, Thai, and Indonesian. We don’t yet know how much the Mars will cost or where they will be available.
In addition to Mars, Line launched a smart speaker in Japan in 2017 called the Clova Wave. Line also announced a series of kid-targeted speakers called the Champ, featuring anthropomorphized Line characters Brown (a bear) and Sally (a baby chicken), but we haven’t heard anything about them since. Line is perhaps best known for its messenger app and social media platform, which is popular in South Korea.
After decades of false starts, Artificial Intelligence (AI) is already pervasive in our lives. Although invisible to most people, features such as custom search engine results, social media alerts and notifications, e-commerce recommendations and listings are powered by AI-based algorithms and models. AI is fast turning out to be the key utility of the technology world, much as electricity evolved a century ago. Everything that we formerly electrified, we will now cognitize.
AI’s latest breakthrough is being propelled by machine learning—a subset of AI which includes abstruse techniques that enable machines to improve at tasks through learning and experience.Although in its infancy, the rapid development and impending AI-led technology revolution are expected to impact all the industries and companies (both big and small) in the respective ecosystem/value chains. We are already witnessing examples of how AI-powered new entrants are able to take on incumbents and win—as Uber and Lyft have done to the cab-hailing industry.
Currently, deployed key AI-based solutions, across industry verticals, include:
Predictive analytics, diagnostics and recommendations: Predictive analytics has been in the mainstream for a while, but deep learning changes and improves the whole game. Predictive analytics can be described as the ‘everywhere electricity’—it is not so much a product as it is a new capability that can be added to all the processes in a company. Be it a national bank, a key supplier of raw material and equipment for leading footwear brands, or a real estate company, companies across every industry vertical are highly motivated to adopt AI-based predictive analytics because of proven returns on investment.
Japanese insurance firm Fukoku Mutual Life Insurance is replacing its 34-strong workforce with IBM’s Watson Explorer AI. The AI system calculates insurance policy payouts, which according to the firm’s estimates is expected to increase productivity by 30% and save close to £1 million a year. Be it user-based collaborative filtering used by Spotify and Amazon to content-based collaborative filtering used by Pandora or Frequency Itemset Mining used by Netflix, digital media firms have been using various machine learning algorithms and predictive analytics models for their recommendation engines.
In e-commerce, with thousands of products and multiple factors that impact their sales, an estimate of the price to sales ratio or price elasticity is difficult. Dynamic price optimization using machine learning—correlating pricing trends with sales trends using an algorithm, then aligning with other factors such as category management and inventory levels—is used by almost every leading e-commerce player from Amazon.com to Blibli.com.
Chatbots and voice assistants: Chatbots have evolved mainly on the back of internet messenger platforms, and have hit an inflection point in 2016. As of mid-2016, more than 11,000 Facebook Messenger bots and 20,000 Kik bots had been launched. As of April 2017, 100,000 bots were created for Facebook Messenger alone in the first year of the platform. Currently, chatbots are rapidly proliferating across both the consumer and enterprise domains, with capabilities to handle multiple tasks including shopping, travel search and booking, payments, office management, customer support, and task management.
Royal Bank of Scotland (RBS) launched Luvo, a natural language processing AI bot which answers RBS, Natwest and Ulster bank customer queries and perform simple banking tasks like money transfers.
If Luvo is unable to find the answer it will pass the customer over to a member of staff. While RBS is the first retail bank in the UK to launch such a service, others such as Sweden’s SwedBank and Spain’s BBVA have created similar virtual assistants.
Technology companies and digital natives are investing in and deploying the technology at scale, but widespread adoption among less digitally mature sectors and companies is lagging. However, the current mismatch between AI investment and adoption has not stopped people from imagining a future where AI transforms businesses and entire industries.
The National Health Services (NHS) in the UK has implemented an AI-powered chatbot on the 111 non-emergency helpline. Being trialled in North London, its 1.2 million residents can opt for a chatbot rather than talking to a person on the 111 helpline. The chatbot encourages patients to enter their symptoms into the app. It will, then, consult a large medical database and users will receive tailored responses based on the information they have entered.
Image recognition, processing and diagnostics: On an average, it takes about 19 million images of cats for the current Deep Learning algorithms to recognize an image of a cat, unaided. Compared to the progress of natural language processing solutions, computer vision-based AI solutions are still in developmental stage, primarily due to the lack of large, structured data sets and the significant amount of computational power required to train the algorithms.
That said, we are witnessing adoption of image recognition in healthcare and financial services sectors. Israel-based Zebra Medical Systems uses deep learning techniques in radiology. It has amassed a huge training set of medical images along with categorization technology that will allow computers to predict diseases accurately better than humans.
Chinese technology companies Alipay (the mobile payments arm of Alibaba) and WeChat Pay (the mobile payments unit of Tencent) use advanced mobile-based image and facial recognition techniques for loan disbursement, financing, insurance claims authentication, fraud management and credit history ratings of both retail and enterprise customers.
General Electric (GE) is an example of a large multi-faceted conglomerate that has adopted AI and ML successfully at a large scale, across various functions, to evolve from industrial and consumer products and financial services firm to a ‘digital industrial’ company with a strong focus on the ‘Industrial Internet’. GE uses machine-learning approaches to predict required maintenance for its large industrial machines. The company achieves this by continuously monitoring and learning from new data of its machines ‘digital twins’ (a digital, cloud-based replica of its actual machines in the field) and modifying predictive models over time. Beyond, industrial equipment, the company has also used AI and ML effectively for integrating business data. GE used machine-learning software to identify and normalize differential pricing in its supplier data across business verticals, leading to savings of $80 million.
GE’s successful acquisition and integration of innovative AI startups such as “SmartSignal” (acquired in 2011) to provide supervised learning models for remote diagnostics, “Wise.io” (acquired in 2016) for unsupervised deep learning capabilities and its in-house the data scientists, and of “Bit Stew” (another 2016 acquisition) to integrate data from multiple sensors in industrial equipment has enabled the company to evolve as a leading conglomerate in the AI business.
Industry sector-wise adoption of AI: Sector-by-sector adoption of AI is highly uneven currently, reflecting many characteristics of digital adoption on a broader scale. According to the McKinsey Global Index survey, released in June, larger companies and industries that adopted digital technologies in the past are more likely to adopt AI. For them, AI is the next wave. Other than online and IT companies, which are early adopters and proponents of various AI technologies, banks, financial services and healthcare are the leading non-core technology verticals that are adopting AI. According to the McKinsey survey, there is also clear evidence that early AI adopters are driven to employ AI solutions in order to grow revenue and market share, and the potential for cost reduction is a secondary idea.
AI, thus, can go beyond changing business processes to changing entire business models with winner-takes-all dynamics. Firms that are waiting for the AI dust to settle down risk being left behind.
The author is Founder and Partner of digital technologies research and advisory firm, Convergence Catalyst.
Biometric Technology: An Invasion of Privacy or Security Authentication?
By Kym Gordon Moore
Is it really possible that reality is adopting futuristic scanning technology we see depicted in spy and sci-fi movies? It’s no secret that sinister threats involving identity theft and fraud to compromise our security throughout every digitized network are growing more complex and sophisticated each day. New security terrorization is becoming more invasive and cleverly evasive of the most recent updates in our anti-Malware and anti-virus programs. Setting up and maintaining biometric security systems in our wireless, LAN or WAN infrastructures is becoming a critical high-tech application to protect highly sensitive, classified information.
Retina scanning, facial recognition, fingerprint identification and speech recognition are a few of the components found in biometrics. So just what is biometrics and who invented it? Derived from the Greek words “bio” meaning life and “metrics” meaning a method of measuring something, biometric systems are pattern-recognition systems that measure and analyze unique physical or behavioral characteristics for personal identification. Behavioral biometrics include voice and handwritten signature recognition. Physical biometrics includes eye scans, facial recognition, fingerprints and other DNA-related attributes.
There are several variations of theories about who invented and first used biometrics. Some reports note that Joao De Barros, a European explorer is credited with inventing fingerprinting technology in the 14th century. A historical account in 1858, references that Sir William Herschel, a Civil Servant of India is credited with the first systemic capture of hand and finger images for ID purposes. Other reports note that biometrics appeared in the 1800’s by a French anthropologist and police desk clerk named Alphonse Bertillon, who developed Bertillonage, a method for identifying criminals based on physical descriptions, body measurements, and photographs.
Fast forward to today’s innovative technology in biometrics, we are seeing applications in areas…Continue reading