Explainable Machine Learning is a sub-field within Data Science and Artificial Intelligence (AI). It is also referred to as X-ML or XML, and projected to be the next biggest avenue for all AI and machine learning applications in the future. Abacus.ai, a leading AI startup has made substantial progress in the field of Explainable Machine Learning, which has been published in its latest paper. This paper is all set to appear at Neural Information Processing Systems (NeurIPS) Conference 2021, to be held between 7-10 December later this year.

Explainable Machine Learning or XML is tested based on three key parameters – transparency, interpretability, and explainability. For any plain machine learning model to qualify as an XML algorithm, it should be understood using concepts of human-level intelligence. In recent years, significant developments have been made in this area with an aim to bring AI and Deep Learning models out of the conventional “black box’ domains. As per IBM, machine learning models are often thought to be behaving as black-boxes that are hard to interpret.

Role of AI ML in Biggest Industries: The Role Of AI, Data And Analysis In True Digital Transformation In Materials And Chemistry R&D

In its latest paper on XML, Abacus.ai has released the workflow associated with XAI-BENCH. XAI-BENCH is a battery of synthetic datasets for “benchmarking popular feature attribution algorithms.” The synthetic dataset could be configured and re-engineered to simulate real-world data using popular explainability techniques across several evaluation metrics.

AI is becoming advanced and human brains behind this trend associate the evolution to powerful XML techniques which are entrusted to bring computing out of black-box approaches. The black box legacy within conventional AI ML algorithms is so deeply entrenched that it would require much more than publishing papers on XML. Abacus.ai is putting its brain and brawn behind XML models to help scientists and AI engineers understand the various ways they can create an algorithm that humans can understand and evaluate what’s happening inside the ‘black-box’ of the AI ML field.

Role of Explainable Machine Learning in Modern Data Science

Explainable Machine Learning or XML is already influencing the penetration of advanced AI in various industries. Some of the key applications of XML in the modern era have been listed below:

In healthcare and telemedicine: XML is used to optimize image analysis, diagnostics, and decision-making for patient management processes;

In banking and loan approval systems, where XML is used to evaluate credit health and financial fraud risks;

In blockchain and crypto, where XAI  and machine learning algorithms can be used to fully secure and decentralize the “highly sensitive system for storing and processing AI-generated data”, and so much more…

As we continue to trace the next phase of advanced AI growth in the marketplace, it is expected that companies like Abacus.ai would emerge as the top contributors of trustworthy AI abilities that break the conventional mold of black-box modeling.

[To share your insights with us, please write to sghosh@martechseries.com]

The post Abacus.ai Publishes Paper on ‘Explainable Machine Learning’ for NeurIPS 2021 appeared first on AiThority.

Exclusive global partnership combines Trend Micro’s cybersecurity research and expertise with Scamadviser’s world-renowned consumer and enterprise anti-scam guidance

Trend Micro Incorporated, a global cybersecurity leader, announced its partnership with Scamadviser, the leading free-to-use online tool that enables users to check websites for potential risk or fraudulent activity. As the key security vendor to Scamadvisor’s site, the partnership provides Trend Micro with the opportunity to educate a sizeable audience around the importance of combating online scams and misinformation.

Recommended AI News: Malcolm Harkins Joins Epiphany Systems as Chief Security Officer

As a Foundation Partner, Trend Micro will support Scamadviser’s Global State of Scam Report as well as the 2021 Global Online Scam Summit – the organization’s annual event occurring in November. The event brings together the world’s leading Consumer Protection Agencies, Law Enforcement Bureaus, Governments, NGOs, and Commercial Organizations to work together in fighting online fraud by sharing insights, knowledge, and data. In addition, Scamadvisor will join Trend Micro in promoting the company’s 2021 TMIE Global Cybersecurity Summit for University Students that will take place in October, which seeks to promote cybersecurity knowledge sharing and stimulate students’ interest in cybersecurity careers.

Recommended AI News: Alteryx and PwC Expand Strategic Relationship Globally to Address Analytics Automation Demand

“Facing an ever-expanding threat landscape, Trend Micro continues to do what it has always done best – building global partnerships that will help to secure the connected world we all live in,” said Brook Stein, Director of Product Management for Consumer Security Products at Trend Micro. “We are excited to support Scamadviser and the Ecommerce Foundation in their mission to protect consumers across the world against being victims of fraud-related crime. As two organizations that are endlessly driven to diminish the impact of scams on the undeserving, our collaboration is a natural fit.”

SysAdmin Appreciation Day: Top Industry Leaders Share their Insights on IT and Data Ops

“We are very proud to welcome Trend Micro to our network of strategically selected allies. With over 30 years of security expertise, global threat research, and continuous innovation, Trend Micro is a trusted authority in all matters of digital security,” says Jorij Abraham, Managing Director at Scamadviser. “Together we will work harder than ever to combat the growing threat of online fraud and scams.”

The partnership announcement follows the fall 2020 U.S. launch of Trend Micro Check – the company’s free online Google Chrome extension that utilizes the latest AI technology to detect scams. The quick-install extension offers users the ability to identify and avoid online fraud and misinformation in real-time, including suspicious stories and websites, news sources and sales scams. The tool was recently updated with new features, including real-time scam alert notifications and AdBlock, which prevents third party tracking and allows for enhanced privacy online.

Recommended AI News: Mendix Partner CLEVR Debuts Further Worldwide Expansion of Software With A Service

The post Cybersecurity Leader Trend Micro Joins Scamadviser as Foundation Partner appeared first on AiThority.

Customer Experience Leaders Must Deliver Gains That Justify Growing Budgets, Says Gartner 

Customer experience (CX) efforts remain inconsistent in many organizations, but there are signs of greater commitment and execution for 2020, according to Gartner, Inc. Gartner’s 2019 Customer Experience Management Survey revealed that more than two-thirds of CX leaders expect budget increases in 2020.

This is a considerable change compared with the 2017 survey where only 47% of CX leaders expected budgets to increase.

“Organizations have matured in their understanding of the business outcomes that CX delivers,” said Augie Ray, VP analyst, Gartner for Marketers.

Augie added, “As a result, the budget outlook is expected to grow to match. This raises the stakes for CX leaders to select metrics that demonstrate impact and prove the value of CX to business results.”

The survey revealed that CX leaders who know how customer satisfaction drives business results get a larger budget. Those respondents whose firms have established a positive relationship between improving customer satisfaction and business impact are more likely to expect significant budget increases.

“CX leaders should consistently measure the value of improved customer satisfaction to the organization to justify and sustain growing CX budgets,” said Mr. Ray. “This is especially important in a period where many expect an imminent economic recession and organization-wide budget cuts.”

Read Also: Heineken Urban Polo Uses Oracle Cloud To Inject AI Into The Sport Of Kings

To positively demonstrate success and prove the value of CX to business results, Gartner recommends CX leaders take the following actions:

Use customer data to demonstrate CX’s business impact

One of the keys to sustaining attention from leaders and collaboration from peers is to make CX matter in terms of the results that are important to them.

Prove how satisfied customers drive up revenue, lower churn, reduce costs and grow the business by combining customer satisfaction data with the transaction and operational data.

Budget for effective CX execution in the future

Developing a CX budget isn’t just a once-a-year process. Always monitor budget and performance and consistently promote the organization’s CX successes and impact. Plan ahead for the annual budgeting process by collaborating with key stakeholders, gathering data and drafting a persuasive budget to accomplish the organization’s CX goals.

Gartner Marketing Symposium/Xpo provides marketing leaders actionable advice about the trends, tools and emerging technologies they need to deliver business results. Gartner for Marketers analysts addresses the biggest opportunities, challenges, and priorities marketers face today, including data and analytics, customer experience, content marketing, customer insight, marketing technology (martech) and multichannel marketing.

Read More: Visa Acquires Plaid For $5.3 Billion; Pledges To Build A Secured Digital Fintech Ecosystem Globally

The post 74% of Customer Experience Leaders Expect CX Budgets to Rise in 2020 appeared first on AiThority.

AI Marketing is a combination of AI principles and applications directly applied to Marketing concepts to target, acquire and retain customers.

In a short span of time, Artificial Intelligence and Machine Learning (AI ML) have become the central marketable assets for SaaS and Cloud businesses. “Technology Maturity” of the organizations which are already offering and leveraging AI ML and data infrastructure in the Cloud range from start-ups, to innovators and pioneers, to leaders and trend-setters.

The worldwide public cloud services market is projected to grow 17.5 percent in 2019 to total $214.3 billion, up from $182.4 billion in 2018, according to Gartner, Inc. Forrester predicts the Public Cloud market to reach $411 billion by 2022.

Forrester states-

“Fewer, but larger, Public Cloud platform providers and a maturing SaaS ecosystem will dominate Enterprise Cloud spending. CIOs should use this forecast to benchmark the pace and shape of their public Cloud strategies.”

Sid Nag, Research Vice President at Gartner, said –

“Cloud services are definitely shaking up the industry. At Gartner, we know of no vendor or service provider today whose business model offerings and revenue growth are not influenced by the increasing adoption of cloud-first strategies in organizations. What we see now is only the beginning, though. Through 2022, Gartner projects the market size and growth of the cloud services industry at nearly three time the growth of overall IT services.”

SaaS and Cloud businesses are increasingly leveraging AI and Machine Learning platforms to scale their revenue by offering better products and personalized customer experiences. According to Adobe’s Digital Intelligence Briefing, leading companies are more likely to adopt and use AI for Marketing to deliver compelling customer experiences. In the next 18 months or so, by simply looking at companies that have annual revenues between $100 and $150 million, the proportion of AI-driven companies would grow to 24%. The biggest growth factor pushing the adoption of AI within these companies is -Data Analytics, followed by Personalization of On-site content and experiences.

AI Technology RADAR: Shaking the IT Foundations with AIOps- Part 1

Craig Roth, Research Vice President at Gartner stated –

“The increasing adoption of SaaS applications and other Cloud services impacts the management, dissemination, and exploitation of enterprise content. Organizations are steadily — but not exclusively — shifting their content environments to SaaS. Gartner expects that by 2019, the current enterprise content management (ECM) market will devolve into purpose-built, cloud-based content solutions and solution services applications.”

The expanding #SalesTech landscape https://t.co/FYTBFEPX9W #Sales #CRM #CX #SaaS #AI #Cloud #MarTech #DataAnalytics #PredictiveAnalytics #eCommerce #FinTech #Payments MT @MikeQuindazzi pic.twitter.com/HUqTrXEK9A

— Tamara McCleary (@TamaraMcCleary) October 10, 2018

The AI economy worldwide is projected to reach $36 billion in 2019. By 2022, this industry would more than double to reach $79 billion. The AI-enabled retail industry, discrete manufacturing, FinTech, and Product Experience platforms would be the largest adopters of AI and ML tools for their various Marketing and Sales efforts.

According to McKinsey, Internet Services and Software companies that have grown only by 20% or less annually, it’s most likely to cease existing in the next 3 years. Innovating with AI ML Marketing Could hold the key to sustaining brand reputation, and revenue growth– keys to surviving in the cut-throat competition.

Recommended: How ERP Boosts Customer Satisfaction?

That brings us to understand how important AI is for Marketing and driving Sales for high-tech businesses, especially those that provide IT Cloud and SaaS products.

But first, let’s define what is AI Marketing!

We already know the evolution of Marketing Technologies and how MarTech RADAR is growing in size and scale. When these MarTech stacks begin to adopt AI applications to boost the ROI and effectiveness in SaaS and Cloud operations, we see AI Marketing bubbling up. Modern B2B and B2C SaaS Cloud enterprise platforms are unique by virtue of many sophisticated components converging together to make things slightly complex for end-customers to fully understand what part of AI is actually working for them.

Therefore, defining AI Marketing becomes very critical to selling to SaaS and Cloud customers.

Don’t Let Your Data-Driven Marketing Strategy Replace a Human Understanding of Your Customers

Artificial Intelligence Marketing is a combination of AI principles and applications directly applied to Marketing concepts with the objective of anticipating customer’s behavior and acquiring a customer by delivering highly contextual, real-time and personalized customer experiences at any stage of the buying journey. Since a large part of these brand-customer interactions occurs online, it is comparatively easy to mitigate and measure AI Marketing results and improve for the next steps.

Strictly sticking to the automated technologies involved, we can define AI Marketing as a branch of Data Science that bridge the gap between Data Aggregation, Management, Analysis, Governance, Privacy, and Execution in Marketing and Sales processes. A large part of AI Marketing operations involves sifting through and analyzing Big Data from countless sources that can only be managed through geared-cycles of Automation, Data Management, Machine Learning algorithms, and Superhuman talent.

SaaS Cloud Transformation in 2019-2022 Depends on Ease and Pace of Customer Data Analytics and AIOps Integration

Provided that Marketing teams have access to a continuous stream of customer data, AI Marketing solutions can boost the sale of IT Cloud and SaaS products. We’ll be addressing all of the details in the content to follow at AiThority.com.

A leading Marketing Cloud company selling to the global B2C market, Selligent Marketing Cloud, reported 60 percent year-over-year (YOY) growth in its global software-as-a-service (SaaS) bookings, particularly bumping sales in North America. A large part of the revenue is attributed to the Company’s expanded product offering toward AI Marketing platform users. This enhanced product line includes AI Marketing tools – Custom Channels, Advanced Journey Components, deeper CRM integrations, and a more robust AI engine. All these empower marketers to deliver flexible Omnichannel campaign orchestration.

Cloud giant and MarTech Customer Experience leader, Oracle, too, charts a very strategic roadmap to grow its customer base via AI Marketing. Oracle offers various development tools and prepackaged AI solutions that help customers accelerate their adoption of AI and Machine Learning (ML). In addition to packaged suites, Oracle is also embedding AI ML into their Cloud services, such as Autonomous Database. Oracle’s AI-based Cloud is used in leading brands, including Outfront Media, NHS, Hertz, CaixaBank, Macty, Accenture and Snap Tech, IDenTV and Daiwa. Oracle’s Mobile Cloud, in particular, has earned a significant reputation in the industry by virtue of its simple adoption and deployment cycle, generating quicker results for chatbot users.

Relatively new players in the industry such as Nudge.ai, People.ai, XenonStack and others in this space are enabling SaaS Cloud customers with extensive tech integrations over their existing IT infrastructure for Marketing and Sales technology. For example, Akira AI Artifical Intelligence Platform enables you to develop Digital Virtual Agents, Chatbots, Predictive agents and cognitive process automation with minimal standardized AI ML operations for multiple data-centers and managed service on the public Cloud. There are many more that provide AI Marketing tools and the numbers are increasing every day.

Other leading players successfully meeting customer needs through AI Marketing applications include IBM, Conversica, Google Cloud, Microsoft, AWS, Genesys, Salesforce, SAP, Adobe, Zendesk, Pegasystems, and Baidu.

We also find leading AIOps and Computing platforms such as NVIDIA, Tencent, HPE, and others joining the club of AI Marketing platforms through cutting-edge tech partnerships, integrations, and collaborations with top MarTech startups.

The post What Is AI Marketing and How It Impacts SaaS Cloud Industry? appeared first on AiThority.

We live in an era where everything that we see and work with, has a plausible involvement of Artificial Intelligence (AI) and Machine Learning. There are over 15000 different AI technology providers that are enabling enterprises and customers to make the most efficient use of their time and resources. While we hear so much noise in the broader AI, the world of Operating Systems (OS) seems to be keeping a low profile all this while.

In our newly launched AI Technology RADAR, we will cover some of the leading Ops for AI  ML and Big Data platforms that are working like Genie to make AI and ML ubiquitous in everybody’s lives.

Read More: Simplilearn Collaborates with IBM to Introduce Four Master’s Programs in Data Science, AI Fields

In PART 1 of AI Technology RADAR, we are focusing on the top OS for AI and ML for IT. We will cover their Digital Marketing campaigns, AI Tech reviews, and the impact they foresee in the coming months.

But first —

What is AIOps?

AIOps is an IT specialization in an application-based environment where a team of data scientists, analysts, developers, and programmers work with various IT tools. The aim is to improve IT functions using OS for AI, Machine Learning, Big Data analytics, Data Visualization, and Data Privacy. These would help to cover a wide array of Management, Monitoring, Surveillance, Security and Automation platforms. AIOps have been in the industry for the last two decades but only made to stand out in the last 5 years.

The most reasonable AIOps definition we found online is –

“AIOps is the practice of applying Analytics and Machine Learning to Big data to automate and improve IT operations. AI can automatically analyze massive amounts of network and machine data to find patterns, both to identify the cause of existing problems and to predict and prevent future ones.”

This is courtesy, Splunk, a leader in AIOps.

According to a verified industry source, and a MarTech RADAR prospect, AIOps “play a critical role in eliminating the manual component of identifying issues within the IT landscape, a problem that’s compounded by the still siloed nature of the monitoring environment.”

In this article, we will start with some of the top AIOps developers and providers in the market.

Read More: The 3 A.I. Scenarios: Artificial Intelligence, Augmented Intelligence, and Intelligent Automation

Let’s start…

#1 IBM AIOps Platform- OpenScale

IBM’s OpenScale powered by Watson AI is a powerful AIOps platform for enterprise and production AI. At IBM, AIOps analysts consider OpenScale as a readymade solution to solve the “black box” phenomenon of data management and analytics.

In its current platform, IBM Watson OpenScale integrates seamlessly with IBM tools for building and running AI models. These include IBM Watson® Studio and IBM Watson Machine Learning. IBM also provides an open development environment for AIOps teams working with TensorFlow, Keras, SparkML, Seldon, AWS SageMaker, AzureML and more.

#2 Google AI

Google AI team is one of the most prolific and scientifically advanced AIOps research and development teams. Not only are they leveraging AI for enterprise IT customers via Google Cloud products, but also providing much needed AI-push to the philanthropical and environmental causes. Google’s TensorFlow is for everyone who wants to think beyond the usual rut of coding and programming. Popular with Python and DevOps teams already working on Mobile applications, Google TensorFlow allows enough room to build a proprietary Machine Learning library.

Our Resources: EAM for Asset Health: Listen to Your Assets with Predictive Analytics and IOT With IBM

For those who want to make a career in Cloud AI ML, or want to start an AIOps company with Google, don’t miss out on checking the various TensorFlow features.

#3 MoogSoft

Moogsoft AIOps is uniquely positioned in this AI RADAR of OS providers. Today. MoogSoft helps to streamline legacy processes and “maximize your IBM Netcool investment.” Algorithmic Clustering Engine (ACE) provides unique capabilities for noise reduction, real-time algorithmic alert clustering, and modern social collaboration technology. Moogsoft AIOps can also provide immediate relief to the stress that can come with being a Netcool administrator.

Using the power of automated anomaly detection, Moogsoft AIOps makes IT real-time and correlate events and alerts from across your application, network, and infrastructure into actionable situations.

#4 Splunk 

AI ML would be the new foundation of every IT function. In an era where we are focused on customized services, how could AI ML Ops be left far behind? That’s where Splunk drives in with its diverse AL ML capabilities for IT Ops.

Splunk for AIOps is focused on reducing the ‘data chaos’ that we are familiar with in this space. Splunk Ops for AI platform provides a unique plain to customers who find it hard to manage data and embrace analytics for intelligent decision-making. It’s time to break the traditional CI/CD toolchain – and, AI can help achieve the next phase of coding.

AIOps at Splunk is focused on the 3C’s of IT. These are:

• Continuous Monitoring
• Continuous Service
• Continuous Automation

All three Cs flow in a cycle or perennial fashion.

#5 AppDynamics

While reviewing the AI Ops ecosystem, we had to filter tons of content on AI ML and their role in transforming the IT Operations. That’s when we came across a blog from AppDynamics and their product, Central Nervous System. The company has a technical collaboration with another Tech RADAR company, Cisco. Together with Cisco, AppDynamics provides AIOps package in complex multi-cloud environments, optimized for flawless workflow performance and user experience.

We were particularly excited about AppDynamics Cognition Engine. Cognition Engine uses sophisticated ML Algorithms to automate anomaly detection, drastically reduce MTTR with instant root cause diagnostics, and correlate software and business performance metrics so IT teams can swiftly diagnose application performance problems.

# 6 Micro Focus

Micro Focus is an IT Operations Management company that has diversified into an Analytics and Big Data and collaboration solutions provider in recent times. COBOL is where we can find Micro Focus beating the competition hands-down. COBOL Analyzer provides well-reported analysis, intelligence, and visualization tools designed for Micro Focus COBOL applications. AI developers, analysts, and DevOp supervisors could achieve a deeper understanding of the IT application portfolio by using Micro Focus’ secure, centralized repository.

Their Automated AIOps for the digital enterprise platform senses, analyzes and adapts to various levels of data management for better business transformation.

#7 Baidu DuerOS

Baidu DuerOS for AI IT processes was launched in February 2017. Baidu brands DuerOS as an independent team built from the original Duer Team. Currently, the Duer BU team is fully responsible for the technology and product innovation of DuerOS – working extensively across the AI ecosystem in the US and China. As a conversational AI Ops platform, DuerOS offers a versatile computing platform that can be integrated with most connected devices, toys, and computer systems. This allows OEMs to reduce production costs and yet deliver on the AI promises to the new-age customers who want technology at the tip of their fingertips.

The website says,

“DuerOS synthesizes the best of Baidu technologies — speech recognition, image recognition, natural language processing, user profile, and other advanced technical skills — to create one of the most advanced conversational computing platforms available today.”

Read More: Baidu Accelerates Commercial Deployment of Internet of Vehicles Solutions with New Partnerships Unveiled at CES Asia

# 8 HPE

Hewlett Packard Enterprise is driving AI-trained IT operations to simplify architectures, enabling IT teams to better manage and support various functions in a multi-Cloud framework. HPE InfoSight is an AIOps platform for Hybrid Cloud that analyzes and correlates with billions of sensors and connected devices deployed all over the systems. Currently, HPE InfoSight runs with HPE SimpliVity HCI offerings. InfoSight AI operations provide customers with global visibility into detailed system, performance and capacity utilization, powered by predictive data analytics and recommendations for real-time performance optimization.

#9 BMC Software

In 2017, BMC Software strengthened its AIOps platform, TrueSight. TrueSight added ML capabilities for multi-Cloud management, empowering digital enterprises to prepare for the next hyper-growth phase in the Industrial 5.0.  We reviewed BMC’s Automated Mainframe Intelligence (AMI) for AI RADAR and found out some very interesting features that AIOps teams can learn.

For example, BMC AMI connects the mainframe data and information to other Big Data engines and dashboards, enabling IT companies to manage end-to-end applications in real-time. It is constantly ingesting billions of data points and analyzing them for Predictive Intelligence using the most advanced AI ML and Big Data Analytics capabilities.

#10 StackState AIOps

Earlier this year, StackState caught our eyes when they announced few industry-shaking updates for IT OS and DevOps. StackState decided to connect their AIOps with VMware vSphere and Google Analytics. VMware vSphere (previously VMware Infrastructure) is a top-end Private Cloud for many large sized IT companies. By adding Google Analytics to StackState’s AIOps platform, OS teams can track website telemetries such as page views, unique visitors and online transactions per minute. This integration enables I&O leaders to get more control on critical business processes and to create rapid feedback loops from Business teams to DevOps teams.

Bonus Entry: BigPanda.io

We included BigPanda in this AIOps RADAR by reviewing their Open Box ML capability. It has a unique open integration hub and an autonomous anomaly detection layer called “LØ”.

We would continue to hunt for the top AIOps providers and enablers in the industry, look out for our AI RADAR 2019 in the coming weeks.

Read More: BigPanda Announces Significant Customer Adoption and New Executive Appointments

The post AI Technology RADAR: Shaking the IT Foundations with AIOps- Part 1 appeared first on AiThority.

Tell us about your journey into the intelligent tech industry. What galvanized you to join Verint?

I was drawn to technology over 20 years ago when I started with cabling and switching. Watching the evolution of information, data, and applications I was always drawn to emerging markets and companies. Over a decade ago I started with Next IT, a trailblazing organization that was already successfully deploying AI into enterprise companies.

When Next IT began to engage in strategic partnership discussions, Verint was the clear choice for our customers and employees. The reason is simple: Verint was the only company that shared the same philosophy and commitment to driving business value and outcomes. The past year with Verint has only reaffirmed the strength of that commitment, and we look forward to the future of driving actionable intelligence with automation for every enterprise company.

What does it take to start and succeed in a Deep Learning HR Tech start-up ecosystem?

One of the biggest misconceptions in business AI right now is the need for academic expertise. Enterprises feel like they need to hire PhDs in order to have a robust and competitive AI strategy. Our success and experience has run a bit counter to that way of thinking. What we’ve found is that success comes from knowing where to apply research skills and where to apply practical implementation skills. Most AI doesn’t need to reinvent the wheel. It needs to build on what already exists and serve specific business needs.

How do you prepare for an AI-driven world as a technology leader?

We’re at a really interesting inflection point right now with AI. As we’ve become increasingly more reliant on it, we’ve begun to bump against the limitations of the technology. We’re starting to see where AI still falls short of achieving its potential. With this in mind, preparation, on the one hand, is about solving these very practical problems that AI is still experiencing. In the big picture, this demonstrates that even as AI leaders, we’re still discovering how best to share and utilize data across channels and sources to make AI as robust and responsive as possible.

How is the role of product strategists in AI-based HR different from that of a Data Officer/ Data Scientist?

As a product strategist myself, I would say that we’re focused on specific solutions and outcomes. Business bottom lines are our top priority. That applies to the data as well, of course, but data officers are more focused on achieving data integrity. To illustrate with a metaphor: we’re creating the car, and they are providing the fuel. Our work goes hand in hand, and we’re certainly all working towards the same goals, but we play different roles in creating those solutions.

How is AI/ML unlocking the capabilities in Human Intelligence?

A lot of the focus and emphasis has been on how AI will either alleviate daily tasks or accelerate our ability to accomplish those tasks. While those will certainly be outcomes, I think the most interesting impact that AI will have on human intelligence is how it might impact the way that we think, what we see as possible, and how we solve some of humanity’s most pressing problems.

What are the foundational tenets of your AI/ML missions? How could business and society benefit from your initiatives?

We don’t believe in technology in search of a problem. Our solutions always map back to concrete business challenges and help organizations achieve their goals. With this mentality, we enable businesses to provide better customer service and empower their employees and customers alike to find the solutions that they need.

Which AI technologies are most likely to impact the HR and recruitment platforms?

In HR and recruitment specifically, I think the biggest challenge the industry faces right now is in counteracting biases that have been unconsciously programmed into our AI systems. AI offers an opportunity for us to look at hiring and recruitment much more objectively, but we have to be very conscious of the way we are writing these programs and the biases we may be putting in place. If we can move beyond that, we reach a fairer and equitable merit-based system that can still account for diversity and the “X factor” of individuals while (hopefully) challenging the ingrained preconceptions we may have.

How do you see emerging technologies — like the Internet of Things (IoT), Robotics and Cloud Computing – coming together with AI/ML to enhance the ROI in traditional HR companies?

When it comes to technology, businesses need to realize that these systems are all more connected than they first appear. By which I mean, enterprises shouldn’t be imbalanced in their tech investing.

IoT has its role to play in cloud computing, which can benefit your AI strategy. If you attempt to keep these technologies siloed or only invest in one branch, you’re actually minimizing your opportunities to innovate in other areas.

What are your top predictions and must-watch AI/ML-related technologies for 2018-2022? How much of these technologies would be influenced by the socio-economic trends?

Every business needs to be developing their AI capabilities and those that aren’t are already dangerously behind. My prediction is that AI will essentially become IT. It will be a necessary component of your business, just as ingrained but perhaps even more powerful than IT. Of course, for each business that implementation looks different. Deciding where and how to adopt AI solutions should always be dictated by the underlying business needs at play.

To this point, my prediction is less about the technology and more about our relationship to the technology. If you’re not training your employees on how to work with AI, you’re doing them a disservice. Despite all the scary articles out there about AI taking jobs, the human and automated workforces don’t actually need to be in conflict. In fact, they should be collaborating.

Tell us about your AI and Deep Learning research programs.

Our AI research program is focused on applied AI, developing and perfecting Machine Learning technologies for training conversational AI systems like IVAs to respond more accurately and efficiently, for both businesses and their customers. We’ve been developing our Machine Learning technology and algorithms for over 15 years, and we’ve evolved some of the most advanced ML capabilities available.

We use proprietary Machine Learning tools, built in-house, to efficiently create, train, maintain, and constantly improve our customers’ solutions. Just as our machine-learning capabilities have evolved in their complexity, so too have our tools grown more powerful, allowing us to quickly and accurately deploy the most robust IVAs in the enterprise.

We focus less on deep learning because we believe it’s imperative to have humans in the loop. The integrity of our machine intelligence relies on human participation. Humans validate the quality of a machine’s learning, the way it prioritizes, and the quality of its “thought.”

For our researchers and our product-development teams, intelligence is about more than automation. Intelligence is actual learning. It is iterative and constantly evolving. To be intelligent, machines need human input at various junctures of the learning process even as these processes are increasingly automated.

What’s the “Good, the Bad, and the Ugly’ about AI? How do you prepare for these situations at Verint?

The Good is that businesses are more open to AI and its capabilities than ever before. They’re also truly beginning to understand how AI can best be applied in ways that help solve their business challenges and empower their workforce. At the same time, technology is improving all the time. It’s continually reaching new levels, which allows businesses to better serve their customers.

The Bad is that even though the technology is so new, businesses already have scar tissue from failed implementations. Unfortunately, amid all the AI hype, some vendors try to get away with selling technology that isn’t actually transformative… because, their AI doesn’t map back to real business questions, it leaves some enterprises with the impression that AI isn’t right for their needs.

The Ugly is the same phenomena but from the AI-provider’s perspective. Sadly, we’ve seen big promises from startups — and even major corporations — about how transformative AI can be. Not nearly enough has been said about how important it is to set clear goals and expectations and how to apply AI. Misleading characterizations about AI’s capabilities and potential and implementations that fail to meaningfully contribute to customer success give us all a bad name.

Read Also: Jumpstart 2019: Interview with Rich Kahn, CEO and Co-Founder, Anura

Do you think “Weaponization of AI/ Intelligence” is the biggest threat to mankind now?

AI is first and foremost a tool, and I believe that it can be applied to address any number of pressing issues that face humanity, from climate change to healthcare, to even more equitable social structures. At this point, humans remain in control of the acts performed at scale by AI — so it is on us to ensure that its power is being used for good.

Thank you, Tracy! That was fun and hope to see you back on AiThority soon.

• About Tracy
• About Verint

About Tracy

Tracy Malingo is Senior VP of Product Strategy of Verint Intelligent Self-Service, a division of Verint, where she provides strategic and operational vision on the company’s extensive and innovative conversational AI-suite. Tracy believes in delivering intelligent solutions to customers and employees so they can build trusted relationships and unlock value together.

Previously, Tracy was president of Next IT, the provider of conversational AI for the enterprise, where she guided conversational AI into the mainstream. Tracy’s compelling blend of business acumen and technical expertise enables her to relate to all elements of the industry and articulate the need for machine intelligence throughout an organization.

About Verint

Verint is a customer engagement company. They organizations simplify and modernize customer engagement through their market-leading cloud and hybrid solutions. In fact, Verint has one of the broadest portfolios of customer engagement solutions available today. They leverage the latest in artificial intelligence and advanced analytics technology to help customers unlock the potential of automation and intelligence to drive real business impact across your organization. Today, over 10,000 organizations in more than 180 countries, including over 80 percent of the Fortune 100, use Verint solutions to optimize customer engagement and make the world a safer place.

The post Jumpstart 2019: AiThority Interview Series With Tracy Malingo, SVP of Product Strategy at Verint appeared first on AiThority.

Deep analysis of the Big Data Analytics/matrix helps to gain the hidden benefit from the reports.

Consumers are overwhelmed with offers for products and services, especially while shopping online. It’s very common for a customer to see advertisements for products that they may have recently searched for, but also sometimes the marketing emails and pop-up ads are not necessarily suitable with the consumer’s interests and may come across as more annoying than actually useful.

More of customer service is the aspect that is vital in any of the fields to rule the market – Unless your customer is satisfied your business is not going to reach the new level.

Isn’t this a truth?

So, to judge that was a customer is liking and what are the kinds of stuff that are driving them to your set of products is worthy of analysis.

Big Data analytics is a term used to refer the large data sets that are too complex for traditional data-processing application software to adequately deal with. Big Data Analytics is the process of collecting, organizing and analyzing huge volume of data to discover patterns and other useful information for organization use.

Are You Not Using Big Data Analytics Yet? You Are Surely Missing out a TON!

The gigantic data matrix helps to enhance the potential and growth of the company. Deep analysis of the data matrix helps to gain the hidden benefit from the reports. Many companies are reporting positive changes while implementing big data analytics initiatives. Researched advantages using the benefits of big data include the following:

• The capability of making better decisions
• High Productivity
• Cost-effective operations
• Personalized effect on the customers’ needs
• Monitoring the sales highs and lows
• Upgraded customer service

Why for You It’s a Right Time to Embrace Big Data Analytics?

With real-time Big Data Analytics, you can step ahead of the competition or the moment can be notified to your direct competitor that you are changing the strategy to be the market leader. Service improves dramatically, which could lead to a higher conversion rate and more revenue. When companies monitor the products that are used by their customers, it can proactively respond to upcoming outcomes – successes or failures.

“New strategies are noticed very immediately and it surely affects the Outcomes.”

Brands need to take a more strategic approach to drive sales and delivering excellent customer service stem to give customers an effective experience. Companies may analyze a huge set of complex data sets from various sources to gain important apprehends of customer behavior and use such feedback to drive sales and provide better customer service with big data analytics.

Recommended: Merging Boundaries Of Data Science And Information At UC Berkeley

Impact of Big Data on Customer Service & Experience

Better Contrive Is the Result of Better Analytics

The use of big data analytics is separating through transactional data or a customer’s purchase history. Such data may reveal how much a customer has spent on something, how often he or she makes the purchase, and—most importantly—on which products or services. This kind of data is crucial for making marketing offers to customers for future purchases as well as recommendations based on customer preferences.

Nordstrom, for example, is developing stores that integrate data collections into a brick-and-mortar environment. It displays what customers have purchased before, whether at a store or online, and generate recommendations essentially serving as personal shoppers.

More Personalized Customer Experience

Big data analytics further present an opportunity to personalize and customize the customer experience. Therefore it offers a chance for companies to interact with their customers by specifying suitable products and services according to the customer’s needs. This helps to expand greater sales, greater customer satisfaction, and sustained brand loyalty.

“Transactional + Behavioral + Social Data = Complete Customer Satisfaction”

For example, financial management and personal banking websites may track a customers’ spending habits and offer cash rewards or other incentives when they spend money, while health-management sites or even devices can provide feedback and encourage people who are monitoring their diets or tracking their vital signs.

Read Also: AI In Recruitment: Welcome To The Realm Of Uncertainty

To Wrench Social Media as a Power Tool

Customer feedback surveys, call transcripts, and most any text produced by the customer in an exchange with customer service agents along with the SMS, social media posts, and chats included are vital sources of big data analytics which allows optimizing customers’ engagement.

Especially, big data gathered from social media activity can provide tremendous spectacle into customer concerns and directs the companies towards identifying and fixing customer service issues. Contact hubs can be reorganized to accommodate high demand channels, and managers can train their agents to work more on channels that receive the most customer contact.

For example, if Twitter proves to be a high demand channel for a certain brand, a company can heavily focus on it. Companies may also use such analytics data to carry out better marketing campaigns on the most in-demand social media sites, serving to customers’ preferences.

Delivers Valuable Feedback

Big data analytics can also show which channels are most frequently used by customers and also the ones which need better engagement, thus providing an additional opportunity for brands to optimize their strategy. As customers are more connected to brands on the channels, it is vital to collect, analyze, and treat all data in order to provide employees with the right tools for understanding so they, in turn, may provide efficient customer service.

By understanding customer behavior, companies can resolve issues more efficiently as big data will enable representatives to offer solutions precisely without a need of any queries.

For instance, Big data is now integrated into CRM systems with the goals of improvised customer analysis, defining a better picture of customer-facing operations, good decision making, and helping in predictive modeling.

Promote Personalization

The concept of big data creating more personalized experiences may seem like a combination of similar technologies.

For example how chatbots are used to boost customer satisfaction. More a fashion chatbot for a brand like H&M helps a customer, the more it learns about their preference in terms of products. The chatbot is then able to come up with personalized product recommendations as a result.

While marketers aren’t expected to rely on robots, they are expected to regularly gather data from customers for a more personalized experience.

Beyond the chatbots, Amazon’s recommendation engine is a relative example of personalized recommendations via data collection. The personalization engages the consumers, and so the key for marketers to deliver the relevant recommendations can be efficiently achieved.

Summarizing the Thoughts

Consistency is key when it comes to provide a meaningful customer experience and build long-term loyalty for the brand. Many businesses have successfully credited big data analytics in order to deliver the consistency across the board.

Businesses with a real-world physical presence must find a way to drive out a consistent experience across digital spaces. If businesses can maximize their gains from a single view of the customer, they will be positioned to deliver an exceptional experience that delights the customer each and every time.

Regardless of industry, big data analytics make all the difference in achieving the relationships that the businesses seek to have with their customers. By capitalizing on these offerings, you’ll know how to improve your customer service and create an experience that is beneficial for everyone.

Each of these points speaks for the need of marketers to make data-based decisions. From increased opportunities and sales to lead the existing marketing campaigns, many marketers have yet to tap into the power of big data. Yet if the upward trend towards adoption of big data analytics is a sign of things to come, those who get on board today most certainly have an edge over their competition.

Recommended: Interview with Tuomas Rasila, CTO & Co-Founder at Vainu

The post 5 Shrewd Ways of Using Big Data Analytics To Enhance Customer Service appeared first on AiThority.

Accept the reality that the AI-for-marketing techniques and technologies they are checking out today are going to change rapidly and frequently over the next 10 years.

Know My Company

Tell us about your journey into building technologies. What galvanized you to join Gartner?

Joining Gartner was an outgrowth of my previous work as a news reporter and late-‘80s work in marketing roles at mobile pioneers such as GRiD Computing and Geoworks. The overall mission of Gartner is to help end-user enterprises understand the rapid evolution of technologies and how they could harness new and emerging technologies for their businesses.  For manufacturers, it is about helping them understand the requirements of their prospects: businesses and consumers.

How do you prepare CMOs and other marketing leaders for an AI-driven world?

There are three main factors that all CMOs and marketing leaders need to keep in mind when it comes to AI:

• Acknowledgment

Acknowledge and make sure your team acknowledges that they are engaging with these tools at a very, very early stage in their development. That means accepting the reality that the AI-for-marketing techniques and technologies they are checking out today are going to change rapidly and frequently over the next 10 years.  This ‘reality’ should be viewed as a truly wide-open opportunity for the right sort of marketer: one who is open to experimentation and has no concerns about working without a net, so to speak.

• Focus

When prepping for an AI-influenced world, focus on getting small projects right first. Do not try to solve all of the problems or challenges your marketing department has. These “small things” should be those tasks that require a lot of manual analysis and data-wrangling: think building/maintaining customer personas and segment building.

• Reach

Reach beyond marketing to other business units when beginning your path to AI-assisted marketing. The utilization of all customer data within an organization is crucial to developing effective AI-for-marketing strategies.

Your marketing “emerging trends” team should include reps from Customer Service, Finance (we always need the CFO to be involved early), Sales and, of course, IT. Getting input from multiple stakeholders might seem counterintuitive at this stage but you need these units to be part of the planning and investigation.

Read Also: Maize Analytics Founder and CEO Presents at HHS “Data Min(d)ing: Privacy and Our Digital Identities” Conference

What are your top marketing predictions and must-watch technologies for 2018-2022? How much of these technologies would be influenced by the adoption of AI/Machine Learning?

Real-time and event-triggered marketing engagements, conversational marketing, and predictive marketing engagements. Each of these requires the ability to effectively extract and recognize patterns in customer behavior and expectations in huge amounts of information — a task that’s just beyond any number of humans (maybe, even data scientists!).

Recommended: A Marketer’s Glossary to Blockchain Technology

In particular, I would say, that the natural-language processing requirements for driving voice interactions on mobile apps and, ultimately, web apps or through chatbots (aka conversational marketing) don’t happen without investments in AI/Machine Learning.

How is AI/ML transforming Marketing and Sales Practices?

Right now the impact is a mix of deer-in-the-headlights fear (the machines are taking over!) and wildly utopian fantasy (AI/ML will solve all problems!).

But, we’re also seeing a growing sense that AI/ML represent a new set of tools to deploy. And that’s the right approach. The meme of big data that consumed so many for the past few years is now meeting the reality that AI/ML techniques and technologies can help marketers wrestle the big-data beast to the ground and isolate the data points that matters.

That’s heartening…

Recommended: Is A Virtual Workplace Really Possible?

What impact will AI have on key roles within the marketing organization?

AI will ultimately free marketers to focus on creating the best environment for sales to occur, freeing them from the challenges of assembling, analyzing, and deriving actionable insights from the mass of data their companies are collecting.  Will some current job titles be at risk?

Probably.

But, smart marketing teams will see this and look for ways to redeploy the people in these roles.

Why is mastering data and analytics so critical today for all businesses?

Accountability and the ability to use a fact base — not guesswork or “hunches” — to determine business strategies and tactics.

Read Also: Activating Customer Data with AI-Based Technologies in Business

Who are best suited to adopt and measure the business outcomes of deploying AI?

Initially, I would say marketing, customer service, and sales, further out, as technology and models mature, product development.

Would we see the creation of a specific AI-related Executive/Office — like Chief AI Officer?

I hope not!

I mean if you play that out, you’d actually need an AI engine to be a “Chief AI Officer.”  I don’t think adding another C-suite position is necessary. AI/ML techniques and technology are tools.

Do we need governance? Absolutely. But, it should be multidisciplinary, not the provenance of a single officer or department.

Elaborate on your playbook for company-customer interaction. How would digital transformation journeys change with the newer technologies? 

Relevance (for personalization) and predictive engagements (convenience and consistency, which have knock-on benefit of being better at delivering what customers actually need/want, as opposed to what you hope they need or want).

How potent is the Human-Machine intelligence for businesses and society? Who owns machine learning results?

Seriously, that’s way beyond my pay grade!

The potential for human-machine intelligence is significant — good and bad. As marketers, we have to start with the notion that the consumer’s data isn’t owned by anybody other than the consumer. The data is exploited by us as marketers, organizations, governments, etc., but it’s the customers who own it. We are only borrowing it. That’s why the GDPR is so important.

The Crystal Gaze

What AI/intelligent tech start-ups and labs are you keenly following?

Some of those at the top of our lists are in this report – “Cool Vendors in AI for Marketing”. Computer vision — particularly for visual search and AR applications.

What technologies within AI and computing are you interested in?

Natural Language Processing (NLP), Computer Vision, Neural Networks, and Deep-Learning Systems. And of course, Machine Learning.

Which industries do you think would be fastest at adopting AI/ML with smooth efficiency? What are the new emerging markets for AI technology markets?

Besides marketing? Financial services use AI/ML for assessing risk, while manufacturing/supply chain for process improvements.

What’s your smartest work-related shortcut or productivity hack?

You’re definitely asking the wrong guy!!! 🙂

Delegate intelligently. Collaborate freely.

Tag the one person in the industry whose answers to these questions you would love to read:

Tim Cook.

Outside the industry? Brian Eno.

Thank you, Michael! That was fun and hope to see you back on AiThority soon.

• About Michael McGuire
• About Gartner

About Michael McGuire

Michael McGuire is a research vice president for Gartner’s digital marketing research group, Gartner for Marketing Leaders, where he is responsible for the mobile marketing research agenda. Mike specializes in mobile marketing, guiding digital marketers on how context, community, location and time — combined with a consumer’s purchase history and purchase intent — are changing the relationship between consumers and brands. Previously, Mike worked with mobile computing startups in the San Francisco Bay Area and Southern California.

About Gartner

Gartner is a trusted advisor and an objective resource for more than 15,000 organizations in 100+ countries.

The post AiThority Interview Series With Michael McGuire, VP Research, Gartner for Marketing Leaders appeared first on AiThority.

In an Agile, Constantly-Transforming Future, the Most Important Skills for an Individual to Be Equipped with Is Learning to Learn

A joint report by the Secretariat of the All Party Parliamentary Group on AI, Big Innovation Centre, and professional services firm KPMG was launched today – identifying the key skills individuals and organizations will need if they are to survive and thrive in the unfolding future.

Read More: Digitalization Spurs Disruption and Growth in the Automotive Services Market

The report finds that jobs in the labor market of the future will look very different from today and the transformation is likely to be dramatic. It also calls for companies and governments to equip citizens and employees for that future, to help them learn the new skills needed to be relevant in a world of constant transformation.

The report identifies three key skills categories people will need in the future:

• skills to build/manage AI
• skills to work with AI
• skills to live in an AI-driven society

There will be an increased demand for “broad” skills and the most valued quality in people will be adaptability and the ability to learn.

At the time of this announcement, Niki Iliadis, Innovation and Policy Foresight Manager at Big Innovation Centre, said, “It’s now time for good policy makers and good companies to make sure people are ready for a future filled with AI.”

“In an agile, constantly-transforming future, the most important skills for an individual to be equipped with is learning to learn – the ability to process new information into knowledge. The borders between hard skills such as computer science and digital, and soft skills like creativity and problem solving, are merging together.”

AI will be key to this. Not only will it find personal solutions to help individuals develop their talents, but it will also be able to match these to a specific task.

Many aspects of our society will be revolutionized by AI. The pessimistic view is that many jobs will be automated, causing the much feared ‘AI replacing humans’ scenario. The optimistic view is that it represents an opportunity for unprecedented progress and an exciting opportunity to explore new jobs and frontiers.

Iliadis added, “Simply put – whether you are a doctor, a teacher, or an accountant, you will deliver the same outcomes, but with an all-new job description. It’s the way we work hand-in-hand with AI that will make the difference.”

Recommended: Mercedes-Benz EQC Trailblazes Cockpit of the Future

Robert Bolton, Head of KPMG’s People and Change Centre of Excellence, said, “The trick is to create organizations and societies that grasp the reinvention of workplaces to make the most of what humans can do, working with AI. This is not simply a challenge of coding, but of leadership, design, imagination, and creativity.”

International companies are already developing programmes such as KPMG’s Workforce Shaping, in which clients learn to identify possible future scenarios and to use multiple mindsets.

Read More: Telit and Wind River Provide Industrial IoT Users with an Industry-leading, Secure …

The report calls for policymakers, organizations, and the wider society to work together to incentivize, encourage, and reward skills across the spectrum – helping build individuals fit for the agility of the future.

The report comes following an APPG AI evidence session held on 26 February in Westminster, focusing on the impact of AI on Skills. The All-Party Parliamentary Group on Artificial Intelligence (APPG AI) was set up in January 2017 with the aim to explore the impact and implications of Artificial Intelligence.

Read Also: Inspirit IoT Awarded Phase II NSF SBIR Grant to Support Development of DNN Architect

The post Most Important Skill for the Modern Age? Learning to Learn appeared first on AiThority.

Today, even for professionals who are not data scientists, it would be helpful to know basic ML concepts, such as clustering, classification, and forecasting.

Know My Company

Tell us about your interaction with AI and other intelligent technologies in your daily life.

As the Head of Data Science at Lucidworks, it’s my job to build AI solutions into our search platform called Fusion and provide a better search experience to our customers.

As a data scientist, how do you see the raging trend of including ‘AI in everything’?

We do see that AI can bring big KPI growth and fast click-through rate lift based on our customers’ feedback just from turning on the AI features in Fusion. That’s the way to go to gain competitive advantage and save human resource costs.

What are the most challenging aspects of working with AI/Machine Learning? How do data scientists turn this into a goldmine of opportunities for the industry and customers?

There are two major challenges customers face before using Fusion.

1. The implementation time of ML models are usually very long. It’s not uncommon for it to take at least six months for engineers to implement an ML model, built by data scientists, into production at large scale.
2. The ML algorithms need to be robust and easy to use. Extra effort is usually needed to put into the algorithm to make the models robust on different dataset samples and easy to tune for business users.

Fusion has many OOTB solutions that shorten the implementation time drastically and we have built several algorithms in-house that are easy to tune and have higher quality than existing open source ML solutions.

As a mentor in the tech industry, how should young marketers and sales professionals train themselves to work better with AI and virtual assistants?

Today, even for professionals who are not data scientists, it would be helpful to know basic ML concepts such as: clustering, classification, and forecasting. The deeper understanding of ML methods, the better you can connect your business problem with the right AI tool.

Any company that wants to put AI as a top priority should make sure there is a direct communication channel between C-level executives and data scientist group leaders.

If interested, some basic data extraction and visualization skills can be fun and helpful to have, so that you can easily use the AI tool to perform your own analysis and reports.

But overall a great AI tool should make the consumption of ML models seamless for the user.

How much do data scientists interact with the company’s business leaders and the decision-makers?

As an AI-driven software company, Lucidworks data scientists have very close interaction with executive-level decision makers to make sure every party is clear on what is possible to enable with AI. It’s also beneficial for data scientists to be aware of the marketing focus or any direction changes in the company. I think any company that wants to put AI as a top priority should make sure there is a direct communication channel between C-level executives and data scientist group leaders.

How do you measure the success/failure of your work at Lucidworks?

The ultimate measurement is our customer satisfaction. The data science team is in direct contact with our users to get feedback about our AI features.

Can they solve their problem at hand with the minimal effort to setup?

What’s the quality of the results?

How about the job running speed?

We ask those questions and gather information, often with user review results, to better understand our performance and provide fast improvements if desired.

How do you consume information on AI/ML and related topics to build your opinion?

Visualizations are fast and powerful tools to help understand the data distribution, compare different options, and make conclusions. Keep in mind that rarely any ML model can provide 100% accurate results but the models can automate many different manual processes and increase business efficiency. It’s also good to pay attention to the assumptions of the model or tests to make sure the model results are aimed to answer your questions.

How can companies further improve their search functionality by using Natural Language Processing (NLP) and AI? What makes Lucidworks a leader in this space?

Lucidworks delivers AI-powered enterprise search to Fortune 2000 companies both on-premise and in the cloud. Examples of search problems we help our customers include: search on corporate intranets, product search from an ecommerce website, or general info search such as search on Reddit.

Our flagship platform is called Fusion, it allows companies to incorporate intelligent search features such as product recommendations, spell-check, auto-suggestions, and query rewriting into their applications without having to build their own systems from scratch and enable them to better compete with the likes of Amazon or Google.

Since we focused ML features in the last a few years and enabled many NLP capabilities, our applications are not limited to just search. E.g., we have a suite of e-discovery algorithms such as doc clustering, anomaly detection, topic trend analysis, synonym discovery, which can serve the needs beyond the search department of a company.

And, our ultimate goal is to solve the last mile problem in AI: to make complex data science available to end users directly, without requiring the user to have any background in data science.

What makes understanding AI so hard when it comes to actually deploy them?

Many AI tools’ designs mainly focus on data, not human. Thus, a great AI platform is usually solution based rather than tool based. For example, the entry point of the platform can start with a questionnaire format and let the user point to their problems, so the platform can automatically choose and combine tools or workflows to help solve the problem. Or if a certain problem always follows a certain pattern or process (e.g. risk management, cyber security), then the platform can be built specifically for such problems.

A key differentiator of Fusion to other vendors is that we are providing operational AI that non-data scientists can easily adapt and use it to solve their own problem.

We conduct extensive testing to make sure the default parameters of our OOTB models works on most of the use cases in different scenarios. And we always include a domain expert in the entire software design cycle to make sure it’s easy to use and aiming the right target.

How an end-to-end solution with data capturing of online behavior helps a company better compete with the likes of Amazon on Google search?

Data richness and quality can weigh more than modeling techniques in data science. Knowing that, in Fusion, we are providing end-to-end solutions from data capturing to result interpretation.

For example, Fusion can automatically capture user online behavior such as search, click on products, add to carts and purchase. That information is ingested and stored in Solr, then automatically transformed into a format that our ML model can consume. In the backend, we use Spark to run ML models such as recommendation, LTR, query intent classification, query analytics and spell checking.

At last, present results in our UI dashboard and directly connect to a pipeline to help improve search relevance at query time. The whole process is very streamlined and easy to setup, which makes AI more operational, rather than waiting for months or even years to see fruit from a data science project, you can see big impact in a short time frame using Fusion.

Which is harder – choosing AI or working with them?

There can be a big difference in implementation time and usability between a good AI platform and a bad one. Choosing the right one can make your life easier and you’ll see improvements in a short period of time. On the contrary, a bad AI software choice can waste a lot of time and require more human input than needed, it may even reduce business efficiency if the investment is too big. Always compare different vendors and make sure the vendor of choice has a great support and advisory program.

Would most businesses turn to AI eventually for better performance?

Not necessarily, again, depends on the balance between AI investment and reward. Choose a software that requires a long implementation period, because putting software on the shelf without adaptation can be a big waste.

Where do you see AI/Machine learning and other smart technologies heading beyond 2020?

As for trends in the ML industry, in recent years, with the healthy development of the open source community, more and more analysts shift from traditional analytics tools to open source languages such as Python, Scala and R. Large scale adoption of open source tools will keep happening beyond 2020.

On the methodology side, it’s worth mentioning the fast growing DL community. New DL architecture comes out every month. A graduate student can easily build a DL model that beats state-of-the-art models traditionally built by a big team. But I have to admit, because we are still lacking the knowledge of the underlying math of “why” and “how” DL works so well, we still need to use different tricks while building models from scratch to prevent overfitting and burning a lot of GPU power. Those are the factors that makes DL in production and large scale usage hard.

That’s why in our R&D practice at Lucidworks, we always design the research experiments to compare traditional methods vs DL methods. Because if we have to run a model on a GPU farm for a week to increase prediction accuracy by less than 2%, then the traditional way can be a more cost-effective solution. Combine that with each customer’s cost concerns, we are able to provide either a traditional solution or a DL solution.

Given the fast progress of DL research, a breakthrough may come around 2020 or even sooner to make DL models more tractable and easy to tune, thus making DL in production easier.

The Good, Bad and Ugly about AI that you have heard or predict

The Good: reduce manual work, lift KPI, provide preventative alerts etc. and can contribute to many industries such as health care, banking and ecommerce.

The Bad: high cost if you choose the wrong AI platform, long implementation, and adaptation time.

The Ugly: AI community need to pay attention to ethics problems caused by AI, such as cyber security, fake tweets, and using robots as weapons.

The Crystal Gaze:

What AI start-ups and labs are you keenly following?

OpenAI, fast.ai, H2O, PipelineAI

What technologies within AI and computing are you interested in?

All kinds. It ranges from unsupervised, semi-supervised to supervised ML methods.

Recently I’ve been especially interested in DL-backed NLP methods and different encoding techniques. I’m also using Spark with Scala daily to build ML jobs in Fusion and found it’s a very good tool to bridge the gaps between data scientists and engineers.

As a tech leader, what industries you think would be fastest to adopting AI/ML with smooth efficiency? What are the new emerging markets for AI technology markets?

Industries like marketing and ecommerce are more equipped to adopt AI/ML because they are less constrained by regulations to use open source tools.

Certain departments at banking and pharmaceutical companies are changing too. A lot can be done in the area of IoT, in addition to data collection and summarization. I can see the healthcare industry will have a revolutionary change lead by current DL-driven technologies.

What’s your smartest work related shortcut or productivity hack?

I followed several great data scientists on LinkedIn, if at least two of them shared the same blog or paper, I better read it to keep up to date with the most recent AI developments.

Tag the one person in the industry whose answers to these questions you would love to read:

Mike Tamir, head of data science at Uber.

Thank you, Chao! That was fun and hope to see you back on AiThority soon.

• About Chao
• About Lucidworks

About Chao

Chao is a data scientist with over 10 years of analytical experience in both academia and industry. She currently works at Lucidworks, an enterprise search engine company, to help build a new product called Fusion AI, which boasts functionalities such as recommendation, query analytics, automatic document clustering and QA system.

Chao received her phD in Statistics from Virginia Tech in 2012 (dissertation: Bayesian visual analytics for high dimensional data. with 8 publications). After graduation she worked at JPMorgan Chase R&D supporting projects in the areas of transaction text mining, social media sentiment analysis, fraud detection, default prediction and target marketing. Chao also initiated and led the “Robot Modeler” project to reduce predictive modeling time from months to days. She joined SAS in 2015 to help develop a new platform – an in-memory multi-threaded analytic engine that enables fast model implementation calculations on a gridded network.

About Lucidworks

Lucidworks builds AI-powered search and discovery applications for some of the world’s largest brands. Fusion, Lucidworks’ advanced development platform, provides the enterprise-grade capabilities needed to design, develop, and deploy intelligent search apps at any scale. Reddit, Red Hat, Moody’s, Commvault, and the US Census are just of few of the companies that rely on Lucidworks every day to power their consumer-facing and enterprise search apps. Lucidworks’ investors include Top Tier Capital Partners, Shasta Ventures, Granite Ventures, Silver Lake Waterman, and Walden International.

The post Interview with Chao Han, Head of Data Science at Lucidworks appeared first on AiThority.