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          Re: Support chatbots      Cache   Translate Page   Web Page Cache   
Hi Mickey,

The case where I spoke to someone on the phone today is not the one with the chatbots.
I'm referring to the one related to VPC.
          Re: Support chatbots      Cache   Translate Page   Web Page Cache   
Hello,
...
          Sr Java Full-Skype Developer      Cache   Translate Page   Web Page Cache   
TX-Richardson, Title: Sr Java Full-Skype Developer Location: Irving, TX Duration: 1 Year Responsibilities: Design, review, analyze, and modify Chatbots, web applications and other supporting systems as per the business systems’ needs Develop and write code, review other Web Developers’ code for modifications, and constructions including all the web application stack which includes the Frontend Middle-tier, Persi
          What's new in BMC Digital Workplace 18.05 ?      Cache   Translate Page   Web Page Cache   

We are pleased to announce BMC Digital Workplace 18.05 is generally available and packed with new features !

 

 

*All features are applicable to Digital Workplace Advanced with the exception of the three levels of browse categories.

 

Simplified Architecture

 

  • Digital Workplace and SmartIT are now separate installers.
  • MongoDB and node.js (MyITSocial) has been removed.
  • Social data is now stored on the Digital Workplace RDBMS (Oracle & SQL).

Digital Workplace 18.05 is a required upgrade before any later releases. If you would like to upgrade to 18.08 when it's released you must first upgrade to DWP 18.05. There is no ability to skip this upgrade if you would like to move to later releases.

 

Save Multiple Carts

 

Save Carts for a later checkout.

Large Subcategory view

 

Subcategories now include a larger view.

Third Tier Category

 

Browse through three levels of Categories.

Remedy with Smart IT Integration

 

Comments to Catalog Requests will be reflected in their associated fulfillment applications and visa versa. Digital Workplace Requests are also visible within Smart IT.

Asset Management Integration

 

Leverage BMC Asset Management to view and action on Assets (CI's).

 

Owned Assets are visible under the "MyStuff" tab.

Add Classes to Asset Groups

 

Create Asset Groups and retrieve data from BMC Asset Management.

Create Asset Action questions using data from Asset Management

 

Data retrieved from BMC Asset Management can also be used wihthin Asset Actions.

As soon as the Action is launched, questions are populated from the relevant form within BMC Asset Management !

Amazon Web Services Catalog

 

 

Import services from the AWS Service Catalog.

 

Submit as a Request in the Digital Workplace Client.

 

 

Chatbot Requests

 

Enable Chatbot for a Request in the Catalog.

Automatically create the Request in Digital Workplace via Chatbot.

Search for submitted answers

 

Retrieve a Service Request based on question response.

Remove services from My Stuff

 

Remove unwanted services from the My Stuff tab.

Export and import Translated Services

 

 

Quickly localize a service by exporting and re-importing again.

View and edit imported Workflow

 

View imported workflow from BMC Service Request Management and add other activities.

 

View Workflow Relationship

Quickly check what questionnaires and services are related to workflow.

Rebranding

 

We now provide an xarchive binary file which you can open in the re-branding tool and create an IPA mobile application. You must sign this file using the rebranding tool and your digital cert from Apple.

Compatibility

 

For version details of Remedy ITSM Remedy AR  System Atrium Orchestrator HR Case ManagementClient ManagementAtrium CMDBCloud Lifecycle Mgmt please refer to the Digital Workplace Basic and Advanced Comparability Matrix for further details.

 

 

Next Steps

 

For a comprehensive summary of all enhancements please refer to our documentation.

 

Please comment with your favorite feature


          DialogueFlow Chatbot      Cache   Translate Page   Web Page Cache   
Build a Patients chatbot using dialogueflow. The documentation is ready to be used. (Budget: $30 - $250 USD, Jobs: C Programming, Java, PHP, Python, Software Architecture)
          Comment on A Bobo in Sierra Leone: An Interview with Richard Britton III by chatbot      Cache   Translate Page   Web Page Cache   
Photoshop 教學導覽
          [آموزش] دانلود Udemy Becoming a Software Tester - آموزش تبدیل شدن به یک تست کننده نرم افزار      Cache   Translate Page   Web Page Cache   

دانلود Udemy Becoming a Software Tester - آموزش تبدیل شدن به یک تست کننده نرم افزار#source%3Dgooglier%2Ecom#https%3A%2F%2Fgooglier%2Ecom%2Fpage%2F%2F10000

آزمون نرم‌افزار به فرایند ارزیابی نرم‌افزار به منظور اطمینان از عملکرد صحیح آن در رویدادهایی مختلفی که ممکن است در دوره استفاده از نرم‌افزار با آن مواجه شود می‌باشد و به عبارت دیگر پیدا کردن خطاهایی احتمالی یک نرم‌افزار برای عملکرد درست، صحیح و بهینه آن در طول استفاده از آن است. هر چقدر نرم‌افزار بتواند با رویدادها مختلف به صورت مطلوب تر و قابل پذیرش تری چه از نظر عملکرد و چه از راحتی کاربر داشته باشد می‌توان انتظار داشت نرم‌افزار دارای عملکرد بهتری می‌باشد. ر سالهای اخیر آمارهای شگفت‌آوری از سوی مؤسسه (NIST(National Institute of Standards and ...


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          Chatbot success: Ethical interactions key to maintain trust      Cache   Translate Page   Web Page Cache   
Chatbot success: Ethical interactions key to maintain trust
          Why chatbots are the customer service agents new best friend      Cache   Translate Page   Web Page Cache   
In another edition of robots taking over peoples jobs Salesforces Paul Baptist takes a mo
          One-to-One at Scale: The Confluence of Behavioral Science and Technology and How It’s ...      Cache   Translate Page   Web Page Cache   

Consumer and business customers have increasing expectations that businesses provide products and services customized for their unique needs. Adaptive intelligence and machine learning technology, combined with insights into behavior, make this customization possible. The financial services industry is moving aggressively to take advantage of these new capabilities. In March 2018, Bank of America launched Erica, a virtual personal assistant—a chatbot—powered by AI. In just three months, Erica surpassed one million users.

But to achieve personalization at scale requires an IT infrastructure that can handle huge amounts of data and process it in real time. Engineered systems purpose-built for these cognitive workloads provide the foundation that helps make this one-to-one personalization possible.

Bradley Leimer, Managing Director and Head of Fintech Strategy at Explorer Advisory & Capital, provides consulting and investment advisory services to start-ups, accelerators, and established financial services companies. As the former Head of Innovation and Fintech Strategy at Santander U.S., his team connected the bank to the fintech ecosystem. Bradley spoke with us recently about how behavioral science is evolving in the financial services industry and how new technological capabilities, when tied to human behavior, are changing the way organizations respond to customer needs.

I know you’re fascinated by behavioral science. How does it frame what you do in the financial sector?

Behavioral science is fascinating because the study of human behavior itself is so intriguing. One of the many books I was influenced by early in my career was Paco Underhill’s 1999 book Why We Buy. The science around purchase behavior and how companies leverage our behavior to create buying decisions that fall in their favor—down to where products are placed and the colors that are used to attract the eye—these are techniques that have been used since before the Mad Men era of advertising.

I’m intrigued by the psychology behind the decisions we make. People are a massive puzzle to solve at scale. Humans are known to be irrational, but they are irrational in predictable ways. Leveraging behavioral science, along with things like design thinking and human-computer interaction, have been a part of building products and customer experiences in financial services for some time. To nudge customers to sign up for a service or take an additional product or to perform behaviors that are sometimes painful like budgeting, saving more, investing, consolidating, or optimizing the use of credit all involve deeply understanding human behavior.

Student debt reached $1.5 trillion in Q1 2018. Can behavioral analytics be used to help students better manage their personal finances?

What’s driving this intersection between behavioral science and fintech?

Companies have been using the ideas of behavioral science in strategic planning and marketing for some time, but it’s only been in the last decade that the technology to act upon the massive amount of new data we collect has been available. The type of data we used to struggle to plug into a mainframe through data reels now flies freely within a cloud of shared service layers. So beyond new analytic tools and AI, there are few other things that are important.

People interact with brands differently now. To become a customer now in financial services, it most often means that you’re interacting through an app, or a website, not in any physical form. It’s not necessarily how a branch is laid out anymore; it’s how the navigation works in your application, and what you can do in how few steps, how quickly you can onboard. This is what is really driving the future of revenue opportunity in the financial space.

At the same time, the competition for customers is increasing. Investments in the behavioral science area are a must-have now because the competition gets smarter every day and the applications to understand human behavior are simultaneously getting more accessible. We use behavioral science to understand and refine our precious opportunities to build empathy and relationships. 

You’ve mentioned the evolution of behavioral science in the financial services industry. How is it evolving and what’s the impact?

Behavioral science is nothing without the right type of pertinent, clean data. We have entered the era of engagement banking: a marketing, sales, and service model that deploys technology to achieve customer intimacy at scale. But humans are not just 1’s and 0’s. You need a variety of teams within banks and fintechs to leverage data in the right way, to make sure it addresses real human needs.

The real impact of these new tools has only started to be really felt. We have an opportunity to broaden the global use of financial services to reduce the number of the underbanked, to open new markets for payments and credit, to optimize every unit of currency for our customers more fully and lift up a generation by ending poverty and reducing wealth inequality.

40% of Americans could not come up with $400 for an emergency expense. Behavioral science can help move people move out of poverty and reduce wealth inequality.

How does artificial intelligence facilitate this evolution?

Financial institutions are challenged with innovating a century-old service model, and the addition of advanced analytics, artificial intelligence tools and how they can be used within the enterprise is still a work in progress. Our metamorphosis has been slowed by the dual weight of digital transformation and the broader implications of ever-evolving customers.

Banks have vast amounts of unstructured and disparate data throughout their complicated, mostly legacy, systems. We used to see static data modeling efforts based on hundreds of inputs. That’s transitioned to an infinitely more complex set of thousands of variables. In response, we are developing and deploying applications that make use of machine learning, deep learning, pattern recognition, and natural language processing among other functionalities.

Using AI applications, we have seen efficiency gains in customer onboarding/know-your-customer (KYC), automation of credit decisioning and fraud detection, personalized and contextual messaging, supply-chain improvements, infinitely tailored product development, and more effective communication strategies based on real-time, multivariate data. AI is critical to improving the entire lifecycle of the customer experience.

What’s the role of behavioral analytics in this trend?

Behavioral analytics combines specific user data: transaction histories, where people shop, how they manage their spending and savings habits, the use of credit, historical trends in balances, how they use digital applications, how often they use different channels like ATMs and branches, along with technology usage data like navigation path, clicks, social media interactions, and responsiveness to marketing. It takes a more holistic and human view of data, connecting individual data points to tell us not only what is happening, but also how and why it is happening.

You’ve built out these customization and personalization capabilities in banks and fintechs. Tell us about the basic steps any enterprise can take to build these capabilities.

As an organization, you need to clearly define your business goals. What are the metrics you want to improve? Is it faster onboarding, lower cost of acquisition, quicker turn toward profitable products, etc.? And how can a more customer-centric, personalized experience assist those goals?

As you develop these, make sure you understand who needs to be in the room. Many banks don’t have a true data science team, or they are a sort of hybrid analytical marketing team that has many masters. That’s a mistake. You need deep understanding of advanced analytics to derive the most efficiencies out of these projects. Then you need a strong collaborative team that includes marketing, digital banking, customer experience, and representation from those teams that interacts with clients. Truly user-centric teams leverage data to create a complete understanding of their users’ challenges. They develop insight into what features their customers use and what they don’t and build knowledge of how customers get the most value out of their products. And then they continually iterate and adjust.

You also need to look at your partnerships, including those with fintechs. There are several lessons derived from fintech platforms such as attention to growth through business model flexibility, devotion to speed-to-market, and a focus on creating new forms of customer value through leveraging these tools to customize everything from onboarding to the new user experience as well as how they communicate and customize the relationship over time.

What would be the optimum technology stack to support real-time contextual messages, products, or services?

Choosing the right technology stack for behavioral analytics is not that different than for any other type of application. You have to find the solution that maps most economically and efficiently to your particular problem set. This means implementing a technology that can solve the core business problems, can be maintained and supported efficiently, and minimizes your total cost of ownership.

In banking, it has to reduce risk while maximizing your opportunities for success. The legacy systems that many banks still deploy were built on relational databases and not designed for real-time processing, providing access via Restful APIs and the cloud-based data lakes we see today. Nor did they have the ability to connect and analyze any form of data. The types of data we now have to consider is just breathtaking and growing daily. In choosing technology partners, you want to make sure what you’re buying is built for this new world from the beginning, that the platform is flexible. You have to be able to migrate between on-premises solutions to the cloud, along with a variety of virtual machines being used today.

If I can paraphrase what you’re saying, it’s that financial services companies need a big data solution to manage all these streams of structured and unstructured data coming in from AI/ML, and other advanced applications. Additionally, a big data solution that simplifies deployment by offering identical functionality on-premises, in the cloud, and in the Oracle public Cloud behind your firewall would also be a big plus.

Are there any other must-haves in terms of performance, analytics, etc., to build an effective AI-based solution?

Must-haves include flexibility to consume all types of data, especially that which is gathered from the web and from digital applications. It needs to be very good at data aggregation—that is, reducing large data sets down to more manageable proportions that are still representative. It must be good at transitioning from aggregation to the detail level and back to optimize different analytical tools. It should be strong in quickly identifying cardinality—how many types of variables can there be within a given field.

Some other things to look for in a supporting infrastructure are direct access through query tools (SQL), support for data transformation within the platform (ETL and ELT tools), flexible data model or unstructured access to all data, algorithmic data transformation, ability to add and access one-off data sets simply (like through ODBC), flexible ways to use APIs to load and extract information, that kind of thing. A good system needs to be real time to help customers in taking the most optimized journey within digital applications. 

To wrap up our discussion, what three tips would you give the enterprise IT chief about how to incorporate these new AI capabilities to help the organization reach its goals around delivering a better customer experience?

First, realize that this isn’t just a technology problem—it will require engineers, data scientists, system architects and data specialists sure, but you also need a collaborative team that involves many parts of the business and builds tools that are accessible.

Start with simple KPIs to improve. Reducing the cost of acquisition or improving onboarding workflows, improving release time for customer-facing applications, reducing particular types of unnecessary customer churn—these are good places to start. They improve efficiencies and impact the bottom line. They help build the case around necessary new technology spend and create momentum.

Understand that the future of the financial services model is all about the customer—understanding their needs and helping the business meet them. Our greatest source of innovation is, in the end, our empathy.

You’ve given us a lot to think about, Bradley. Based on our discussion, it seems that the world of financial services is changing and banks today will require an effective AI-based solution that leverages behavioral science and personalization capabilities.

Additionally, in order for banks to sustain a competitive advantage and lead in the market, they need to invest an effective big data warehousing strategy. Therefore, business and IT leaders need a solution that can store, acquire, process large data workloads at scale, and has cognitive workload capabilities to give you the advanced insights needed to run your business most effectively. It is also important that the technology is tailor-made for advancing businesses’ analytical capabilities that leverage familiar big data and analytics open source tools. And Oracle Big Data Appliance provides that high-performance, cloud-ready secure platform for running diverse workloads using Hadoop, Spark, and NoSQL systems. 


          HubSpot Conversations: Free Live Chat, Shared Team Email, and Chat Bots      Cache   Translate Page   Web Page Cache   

In this post, you will learn about HubSpot Conversations which is a new feature of HubSpot CRM. It offers tools like live chat, team email, and chatbots.

The post HubSpot Conversations: Free Live Chat, Shared Team Email, and Chat Bots appeared first on I Love Free Software.


          A look at Duplex, Google's creepy AI chatbot      Cache   Translate Page   Web Page Cache   
Google can now use artificial intelligence to make phone calls on your behalf, but is it clever or alarming?
          Solutions Architect      Cache   Translate Page   Web Page Cache   
OH-Perrysburg, Responsibilities: Take technical lead on the team in the ML/NLP domain and keep up with latest industry solutions. Work with teams across the country on laying out designs and an architecture that addresses the business requirements Design, develop, validate and deploy proposed ML/NLP solutions both internal and client facing. Innovate and build new features for our products including chatbots and
          From Tableau to Elastic: How Samtec Streamlined Business Intelligence & Analytics      Cache   Translate Page   Web Page Cache   

Let’s get this out of the way: we’re not the typical Elastic users. While most use Elasticsearch for logging, security, or search; we use Kibana and Elasticsearch for business intelligence across our enterprise from sales to manufacturing. Also, we have applications using Elasticsearch as a primary data store, and our “production” cluster is often running pre-releases to take advantage of the newest functionality. That being so, our origin with Elastic is likely the same as yours — it all started with needing a place to dump a whole bunch of logs.

Samtec builds electrical interconnects found in products ranging from medical devices, servers to self-driving cars. Our group — Smart Platform Group — was born from an area of Samtec that builds high-speed copper and optical interconnects. Samtec’s FireFly™ family of interconnects are a great example. They have 192 Gbps of bandwidth packed onto a device the size of a dime.

samtec_1.png

The process to manufacture Firefly™ requires hundreds of steps. Almost every step gets performed by equipment that produces logs containing volumes of data. For example, one step of the process is to place silicon die onto a printed circuit board. The placement gets measured in microns (1/1000th of a millimeter), and the machine that does the placement holds onto each die with a small amount of vacuum pressure. That pressure gets logged for every placement. Those logs look something like this:

"die_count_x" => "integer"
"die_count_y" => "integer"
"commanded_pick_force" => "float"
"actual_pick_force" => "float"
"commanded_place_force" => "float"
"actual_place_force" => "float

We needed to start with capturing the data that would be lost over time as the machines rotated their logs. Logstash, and by extension Elasticsearch, were determined to be the quickest and most cost-effective solution, requiring the least development and maintenance effort. So we wrote some Logstash configs and started dumping data into Elasticsearch. It may not have been pretty, but it worked and we more-or-less forgot about it. That was late 2015.

Three months went by, then six, then a year — and the data was never touched. Finally, one fateful day a customer request came in with an early device failure “in the field”. We needed to look back to when that customer’s order was running on a specific machine to see if everything looked normal in the production process.

Cue our first Kibana visualization, a simple line chart showing the force used to pick up that small silico die. We knew the build date of the device, so we filtered our chart by date and got something similar to the below.

samtec_2.jpg

Each dip in the top line represents a period where the force seems to have dropped suspiciously low. Naturally, we had lots of questions, but the most pressing was: did the device in question get built during one of those periodic dips? The exact times each order and piece got ran was stored in our SQL database, and we needed to bring the two pieces of data together. Each machine had a vastly different data structure, and the logistics of moving them all into SQL seemed problematic. However, moving tabular SQL data into Elasticsearch was a well-documented path with Logstash. So we wrote another Logstash config to bring in the SQL data and leveraged some Kibana-fu to overlay orders and serial numbers onto some time series charts.

samtec_3.jpg

Given the above, it looked like there were a couple of transactions that occurred during a time of “low pick force”. We were going to have to review those in more depth.

What started as one or two Logstash configs was threatening to turn into hundreds as the number of data sources we wanted to pull into Elasticsearch grew. Rather than continue to spend developer time creating Logstash configs, a product idea was born focused on making Elasticsearch data imports more accessible. Moreover, we wanted to enable self-service Business Intelligence using the Elastic Stack.

Enter Conveyor

Conveyor is an open source plugin that we wrote with data analysts and business users in mind. We wanted a graphical, interactive way for loading data into Elasticsearch. We wanted it to be easily extended to support different data sources, and we wanted it to integrate well into the Elastic Stack.

samtec_4.jpg

So now that any data is just a few buttons away from being in Elastic, what kind of possibilities are within an easy reach?

We’ve replaced our Tableau Dashboards with Kibana ones. In this case a heads-up display for showing order status in a manufacturing center. Auto-refresh meant the display showed new orders near real-time.

samtec_5_6_2.png#source%3Dgooglier%2Ecom#https%3A%2F%2Fgooglier%2Ecom%2Fpage%2F%2F10000

We’ve used Conveyor to pull in bill of materials and inventory data. We put it all in to Graph to quickly trace suspect lots through manufacturing and to identify other affected lots. In this case, we were able to easily determine customer orders (green circles) that consumed a suspect lot (red circle) even though there was a sub-assembly process that occurred midstream (pink circles).

samtec_7.jpg

We’ve also used Conveyor to pull in process control data and analyzed it for anomalies using Elastic machine learning. In this case, we analyze a metric that gauges the health of a test station — and we can easily identify or be alerted when it isn’t as expected.

samtec_8.jpg

Finally, we use Conveyor and Kibana together to build powerful dashboards on all sorts of business metrics, like GitHub Repository Statistics, for our open source chatbot platform called Articulate to cheer-on the increasing downloads and stars!

samtec_9.jpg

What’s Next

We’ve now been using Elasticsearch as a primary data store for our projects for more than four years and though we don’t have big data like some users of Elastic we have broad data. With the introduction of Conveyor, as well as new Kibana functionality like Canvas and Vega visualizations, we strongly believe that the Elastic Stack is the best open source business intelligence platform. To reinforce that we’ll leave you with three thoughts:

  • Joining disparate data sources in a single analytical tool is extremely powerful and compounds the value of your data.
  • Dynamic mappings and re-index capabilities make Elasticsearch an excellent collect now, analyze later data store.
  • Cross-index search enables powerful information gathering on business data. (Want all sales orders, shipments, and contacts for a customer? Just search for their name across multiple indices).

We hope to be back to share more in-depth walkthroughs on using Conveyor with Vega and Canvas, as well as our experience with the new Elastic User Interface (EUI) library. We’re happy to share our experiences so keep an eye out for those here or on our site at https://blog.spg.ai. Also, be sure to check out Conveyor


Caleb Keller, Woo Ee, and Mike Lutz head a team of data lovers, tinkerers, and technology enthusiasts building open-source solutions for Samtec's Smart Platform Group


          A look at Duplex, Google's creepy AI chatbot      Cache   Translate Page   Web Page Cache   
Google can now use artificial intelligence to make phone calls on your behalf, but is it clever or alarming?
          Your banking data was once off-limits to tech companies. Now they’re racing to get it. -- The Washington Post      Cache   Translate Page   Web Page Cache   
Also see Facebook's comment on the latest Facebook + data = bad meme-of-the-week below
"Many of the tech world’s major players have shown similar ambitions in tapping users' financial data, which could help companies lock in customer loyalty, help developers provide more sophisticated tools — and give advertisers lucrative clues into users' offline interests and buying intentions.

Apple and Google provide mobile-payment services that allow users to access financial information and pay for products with their phones. Amazon.com — whose founder, Jeffrey P. Bezos, owns The Washington Post — offers users a credit card issued by JPMorgan Chase. And Google last year announced a deal that would let it review and analyze roughly 70 percent of all credit and debit card transactions in the United States.

“What you purchase is the ultimate predictor of what you’ll purchase in the future. It’s an indication of your stage of life, and there’s a lot that can be packaged up and targeted based off that,” said Mike Herrick, an executive at Urban Airship, which works with companies on digital wallets and mobile engagement. “Everybody’s mad at Facebook, but they’re just one of many participants in this data ecosystem.”"
Facebook's comment, from Facebook taps banks, but for chatbots not purchase data like Google (TechCrunch)
"“A recent Wall Street Journal story implies incorrectly that we are actively asking financial services companies for financial transaction data – this is not true. Like many online companies with commerce businesses, we partner with banks and credit card companies to offer services like customer chat or account management. Account linking enables people to receive real-time updates in Facebook Messenger where people can keep track of their transaction data like account balances, receipts, and shipping updates,” [Facebook spokesperson Elisabeth Diana] told TechCrunch. “The idea is that messaging with a bank can be better than waiting on hold over the phone – and it’s completely opt-in. We’re not using this information beyond enabling these types of experiences – not for advertising or anything else. A critical part of these partnerships is keeping people’s information safe and secure.”"
Your banking data was once off-limits to tech companies. Now they’re racing to get it. -- The Washington Post

           Upsurge Profit by ERP & CRM Software in Saudi Arabia       Cache   Translate Page   Web Page Cache   
السعر: 1 ر. س,
If you want to increase your Income then Alrasmyat, offering best Chatbots Integrated CRM Software in Saudi Arabia to maintain your business services with the help of fully integrated M2M, Augmented reality and AI, expert CRM Software in Saudi Arabia. It helps to improve in consistency through revie... https://olx.sa.com/ad/upsurge-profit-by-erp-crm-software-in-saudi-arabia-ID6O0L5.html
          Most customer queries won't need humans, says firm that only makes chatbots      Cache   Translate Page   Web Page Cache   
A NEW business is entirely devoted to creating chatbots, in readiness for the day when humans are eliminated from the vast majority of customer queries.
          Why Chatbots Are the Future of M-Commerce      Cache   Translate Page   Web Page Cache   

Conversation drives sales and this is a well-known fact. For customers, it is important to have someone to ask questions and clarify doubts, someone who could guide them and recommend them the best option. Today, conversations can be automated, and today there is no need to have a physical person attached to each customer. Nowadays, conversational commerce became a fast-growing buzzword and chatbots play a key role in this field. Today, I would like to discuss why chatbots became so popular and why e-commerce and m-commerce companies heavily invest in it.

What Is a Chatbot?

First off, let’s make sure we are on the same page. What is a chatbot? 
A chatbot is a computer program or an artificial intelligence, which conducts a conversation via auditory or textual methods. It simulates how a human would behave in an automatic way, improving the efficiency of the process.


          用户管理、商品导购、精准营销···chatbots 可不止客服这么简单      Cache   Translate Page   Web Page Cache   
提起“chatbots”你会想到什么?从词语拆分来看,是不是觉得chatbots只能用于聊天,比如客服?实际上,随着技术的不断发展,chatbots的用途越来越广泛,从增强用户界面到创建更个性化的应用..
          Update: qplum - AI-driven Robo Advisor (Finance)      Cache   Translate Page   Web Page Cache   

qplum - AI-driven Robo Advisor 10.5.5


Device: iOS iPhone
Category: Finance
Price: Free, Version: 10.5.3 -> 10.5.5 (iTunes)

Description:

Plan and invest automatically using AI in a portfolio of low-cost, low-risk ETFs.

qplum is an online financial advisor which invests automatically using AI, and data. We invest in portfolios of low-cost Vanguard, iShares, Charles Schwab ETFs. We offer free financial plans tailor-made for your income, expenses, assets, etc. We help build and manage wealth. We are the cutting edge of Robo-Advisors.
Free financial planning, wealth management, Traditional, SEP, Roth IRAs, 401(k) rollovers, retirement, individual, joint, and custodial accounts (UTMA/UGMA), savings goals - We got all financial advice covered!
We have low-risk investment portfolios, high-growth high-return investment portfolios, and everything else for your investing needs.
We offer simple, automated online investment - Download the app for free, get started in less than five minutes, open your account, and let our financial advice make your investments grow.

Who are we?
qplum is a fully automated robo-advisor. We manage wealth online. We are a digital investment advisor which invests through algorithms. We invest in ETFs of US and international stocks, US government bonds, international fixed income, and real estate. Our diversified portfolios use AI and HFT. Think of us as a digital hedge fund, which charges 0.5% annual fees.

qplum was founded by Gaurav Chakravorty, one of the earliest high-frequency traders in the world, and Mansi Singhal, a billion dollar portfolio manager with Brevan Howard, one of the most profitable hedge funds.

We offer:
- AI driven chatbot that offers free financial planning to suit your income, expenses, goals, etc. Now that’s Robo-Advising!
- Diversified ETF portfolios of stock, bond, and real estate ETFs to create wealth.
- Traditional IRA, SEP IRA, Roth IRA, Joint Accounts, 401k Rollovers.
- End-to-end automated trading to save time and effort!
- Account transfer from your existing broker to qplum.
- Monthly auto-deposits to invest every month.
- No additional fee for trading.
- Paperless, online investment experience!

Our strengths:
- AI and data science- We invest through algorithms based on AI and machine learning.
- Stellar portfolio managers and data scientists with proven track record in hedge funds.
- End-to-end automation - Robo-advisor with no human trading.
- Alpha - for returns independent of the stock market.
- Risk management to save you from market crashes.
- Algorithmic execution to buy and sell your products at a better price.
tax loss harvesting to save taxes on investments.
- Daily rebalancing optimized to reduce costs and improve returns.

With this app you can:
- Talk to AI-powered chatbot and get a free financial plan.
- Invest in a blend of our top portfolios.
- Open Traditional, ROTH, SEP IRA accounts.
- Rollover your existing IRA/ 401k into qplum.
- Track the performance of your investments in your ETF portfolio.

Regulated and trusted
- QPLUM LLC is registered with the U.S. Securities and Exchange Commission (SEC) as an Investment Advisor and with the National Futures Association (NFA) as a Commodity Trading Advisor (CTA).
- All Brokerage and Clearing services are provided by, and securities are offered through Apex Clearing Corporation and/or Interactive Brokers, both members of FINRA/SIPC.
- qplum accounts are SIPC protected up to $500,000, including a maximum of $250,000 for cash per customer, against losses resulting from the failure of a broker-dealer. An explanatory brochure is available at www.sipc.org.

The future is free. The future is fair. The future is qplum.

Investment Advisory Services offered through QPLUM LLC. All investments carry risk. This material is for informational purposes and should not be considered specific investment advice or recommendation to any person or organization.Past performance is not indicative of future performance. Read more on our disclaimer and terms of use at www.qplum.co/privacy-terms

What's New

With this update:
- Open TD Ameritrade and Interactive Brokers linked investment accounts
- Support to make transfers above $50K ACH limit with wire transfers
- More detailed Monthly Auto-Deposits summary for invested accounts
- Invest one-time or monthly when opening a new account
- Better context for in-progress events on your invested accounts

qplum - AI-driven Robo Advisor


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Have to create a automation script that automates chatbot conversation and validate with the output in eclipse (Budget: ₹1500 - ₹12500 INR, Jobs: Eclipse, Selenium Webdriver)
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Microsoft Bot Framework can help you to build, connect, deploy, and manage intelligent bots to naturally interact with your users on a website, app, Cortana, Microsoft Teams, Skype, Slack, Facebook Messenger, and more. In this tutorial, we will demonstrate how to connect the Microsoft Bot Framework to your conversation.one omni-channel conversational application, and reuse the code and data created for your Amazon Alexa skills, and Google Home actions to other channels supported by the Microsoft Bot Framework.

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