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How many times have you failed in your life? What a question this is, isn’t it? Unless you are overly genius or optimistic, you will probably answer that 100, 1000, or uncountable times.

And perhaps every time you failed and had to bear the consequences of failure, you thought – “What if I could work like a computer!”

No doubt, the power of computing gives us the ability to automate tasks and reduce failures. The addition of AI enables decisions to be made based on the based-on data. But what if, despite all the hype, AI systems fail to provide anticipated results?

The decision and predictions made by AI systems rely on concepts such as probabilistic methods and statistical analysis. AI considers the uncertainty and variability inherent in real-world data and makes predictions based on the most likely outcomes. To test the algorithms, techniques such as cross-validation and model selection are used to evaluate the system’s performance and identify any weaknesses or biases.

However, there are times when accurately trained AI systems have failed and produced wrong or misleading results. At the fundamental level, our AI models show poor performance metrics. But this article covers 5 interesting examples when well-trained AI models behaved opposite to what we expected.

Case 1: When Microsoft’s Tay Turned Hateful!

Source – The Verge

The incident with Tay highlighted the importance of designing AI systems that can handle and filter out inappropriate or harmful content, as well as the potential dangers of using AI systems that can learn from their interactions with humans.

To prevent this behavior, it would have been necessary to implement stronger filters and moderation systems to prevent the chatbot from learning and repeating inappropriate or offensive content. Ir would have been design ral norms and social expectations, to help it better understand the appropriateness of the content. This could have involved using ML algorithms to identify behavior patterns typical of internet trolls and implementing countermeasures such iting or content blocking to prevent them from overwhelming the system.

In December 2024, Microsoft released Tay’s successor, a chatterbot named Zo. Satya Nadella, the CEO of Microsoft, said –

“Tay has had a great influence on how Microsoft is approaching AI and has taught the company the importance of taking accountability.”

Case 2: When Google Turned a Little Racist!

Source – Boing Boing

The case – In 2023, it was reported that Google’s object recognition software, which is based on machine learning algorithms, was found to have higher error rates when identifying images of people with darker skin tones compared to those with lighter skin tones. This finding was part of a larger study that examined the performance of several image recognition systems and found that they tended to perform worse on images of people with darker skin tones.

What went wrong? The reason for this failure has been that the training data used to develop the image recognition system was biased, meaning that it did not accurately represent the diversity of the population and may have had a disproportionate number of images of people with lighter skin tones. Another possibility is that the system itself may be biased, meaning that it has been designed or trained in a way that favors certain groups over others. This could be due to a variety of factors, including the algorithms used to develop the system, the way in which the system processes and interprets data, and the way in which the system is evaluated and tested.

Overall, it is important to ensure that AI systems are developed and tested in a way that considers the diversity of the population and minimizes potential biases.

Case 3: Does Amazon AI not Like to Hire Women?

The case – Amazon’s AI-powered hiring tool was an automated system designed to evaluate job candidates based on their resumes and recommend the most qualified candidates for a given position. The tool used machine learning algorithms to analyze resumes and to score candidates based on various factors, such as their education, work experience, and skills. However, it was found that the tool was biased against women in several ways.

One way in which the tool was biased was that it downgraded resumes that contained the word “women’s,” such as “women’s studies” or “women’s rights.” This suggests that the tool may have been programmed to view references to women’s issues as negative or less desirable and to score candidates with such references lower than others.

B The tool was also found to penalize resumes that included a higher education degree in women’s studies. This suggests that the tool may have been programmed to view education in women’s studies as less valuable or relevant than other types of education, and to score candidates with such degrees lower than others.

Case 4: Can’t we rely on AI-powered COVID-19 help?

Source- CB Insights Research

The case – In 2023, the UK government developed an AI-powered virtual assistant called “Coronavirus Information Bot” or “CIBot” to answer questions about COVID-19. The bot was designed to provide information and guidance to the public about the virus and was made available through the government’s website and social media channels.

However, it was later discovered that the bot was providing misinformation and incorrect guidance on a number of topics related to COVID-19. For example, the bot was found to be recommending the use of certain unproven treatments for the virus, such as inhaling steam, and was also found to be providing inaccurate information about the transmission and severity of the virus.

What went wrong? There are a few potential reasons why the bot may have provided misinformation and incorrect guidance. The bot was based on outdated or incomplete information about COVID-19, which could have led to the bot providing incorrect guidance. Another possibility is that the bot was not programmed to accurately filter or verify the information it provided, which could have resulted in the bot providing misleading or incorrect guidance.

Case 5: When Google’s Self-driving Car Led to An Accident!

The case – On February 14, 2024 (when some people happily celebrated Valentine’s Day 😊), a self-driving car operated by Google was involved in a collision with a public bus in Mountain View, California. There were two occupants in the self-driving car at the time of the collision, both of whom were Google employees. One of the occupants sustained minor injuries as a result of the collision and was treated at a local hospital. There were no injuries to the bus driver or any passengers on the bus.

What went wrong? According to Google, the self-driving car that was a Lexus RX450h SUV was traveling at a speed of around 2 mph when the collision occurred. The car’s sensors had detected the bus approaching the adjacent lane, but its software incorrectly determined that the bus would yield to the car as it changed lanes. As a result, the car moved into the path of the bus, and the collision occurred. A news highlight given here shows more details.

How Can We Have More Reliable AI?

The present-day AI is certainly reliable, isn’t it? And countless examples stand in support of this. However, when it comes to building a large and public implementation of models that heavily affect the citizens, AI scientists and engineers need to be more careful.

It starts by clearly defining the goals and objectives of the AI model – what it is intended to do and how it is used.

Training data should be diverse and representative of the population that the AI model will be used on. This will help to reduce bias in the model and improve its performance on a wider range of inputs.

Regularly monitoring and testing the AI model using several evaluation metrics is most important. This could include testing the model on different data types, using different evaluation metrics, and comparing the model’s performance to other models.

The present era is about transparent, accountable, and explainable AI. This could include documenting the model’s development process, making the model’s code and data available for review, and providing explanations for the model’s predictions.

Even if you fail in life, you should keep trying. Regularly updating and maintaining the AI model can help to ensure that it continues to perform well and remains unbiased over time. This could include retraining the model on new data, fine-tuning the model’s parameters, and addressing any identified issues or errors.


AI makes p cle. Here’s what we got to know –

Microsoft’s chatbot Tay started producing inappropriate remarks after being deliberately fed with hatred and biases. Implementing filters for systems that work on user inputs is hence critical.

Automatic AI review systems by Google & Amazon showed bias towards particular gender and skin color, probably due to some biases in data.

Chatbots that are popular can often provide misinformation, as seen with a bot run by the UK. Govt.

During a trial run, Google’s self-driving model couldn’t predict the onset of a bus, thus leading to a collision.

(Disclaimer: The images put in this article are solely for informative and educational purposes, along with the sources mentioned. No copyright infringement is intended whatsoever.)

The media shown in this article is not owned by Analytics Vidhya and is used at the Author’s discretion.


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Top Ai Investments Of June 2023

The startup scenario is being changed by bringing in investment and deal activity around intelligent automation and artificial intelligence, big data and machine learning. The data plainly demonstrates that new businesses that had AI as a core product are creating narrow AI tech packed away with the heaviest investment from leading VC firms and investors who are putting vigorously in deep tech startups in big data, enterprise AI and automation. It likewise underscores a great part of the financing going on in domain explicit breakthrough innovations, and not broadly useful AI tech. Let’s look at the top

Tala Security

Amount Funded: $9 million Transaction Name: Series A Lead Investors: TechOperatos

Amount Funded: 10 million Yuan Transaction Name: Series A Lead Investors: Sequoia Capital China is a cutting-edge startup that gives AI based cloud services. It is focused on applying super-calculated scientific research and innovative accomplishments to the business field to make a productive distributed AI framework for enterprise AI ecosystems.  


Amount Funded: $45 billion Transaction Name: Series B Lead Investors: Sapphire Ventures, Scale Venture Partners AllyO is an A.I. recruiter that makes recruiting awesome and effective for everybody. AllyO has brought $45 million up in Series B financing. Investors incorporate Sapphire Ventures, Scale Venture Partners and prior patrons Gradient Ventures, Randstad Innovation Fund, Bain Capital Ventures and Cervin Ventures. They’ll use the funding to further build up the organization’s products and grow its scope.

Amount Funded: $3 million Transaction Name: Series A Lead Investors: Innocells Artificial Intelligence (AI) startup chúng tôi brought $3 million up in an all-inclusive Series A round from Innocells, the innovation hub and corporate venturing arm of Spanish banking team Banco Sabadell. Existing investors include Kalaari Capital, Chiratae Ventures (in the past IDG Ventures India), and Vertex Ventures likewise partook in the round. Its AI engine, chúng tôi is utilized to comprehend complex contexts and discussions and give smart responses through natural dialogues.  


Amount Funded: $2 million Transaction Name: Pre-series A Lead Investors: Pi Ventures Bengaluru-based Wysa raised about $2 million (Rs 15 crore) in a pre-Series A round driven by pi Ventures, with interest from Kae Capital and different investors. The AI-based mental health startup intends to utilize this funding to further fortify its innovation and for expansion.  

Yellow Messenger

Amount Funded: $4 Mn Transaction Name: Series A Lead Investors: Lightspeed Venture Partners Bengaluru-based conversational AI solutions provider Yellow Messenger raised $4 Mn (INR 27 Cr) in a Series A funding round from Lightspeed Venture Partners and noticeable angel investors. The angel investors who took part in the round incorporate Phanindra Sama, originator, Redbus and CIO of Telangana; Kashyap Deorah, founder of Hypertrack; Anand Swaminathan, senior partner, McKinsey and Co.; Prashant Malik, co-founder, Limeroad; Nishant Rao, previous MD, Linkedin India; Kunal Bahl and Rohit Bansal, co-founders, Snapdeal; Monisha Varadan, Zephyr Ventures and Alap Bharadwaj, APAC Innovation, Google. The funding will be utilized towards innovative work. They will likewise put resources into creating cutting-edge profound tech abilities, and extend its customer success teams and nearness in high-potential markets across Asia, the Middle East, and other developing markets.  


Amount Funded: $1 million Transaction Name: Seed round Lead Investors: pi Ventures Bengaluru-based SwitchOn, an edge-AI empowered Industrial IoT organization, brought $1 million up in a seed round of funding driven by pi Ventures, the AI, IoT and Blockchain-concentrated early-stage venture fund. Alongside pi, Axilor and prominent angel investors from The Chennai Angels additionally took an interest in the round.  

Enflame Tech

Amount Funded: 300 million yuan Transaction Name: Investment Lead Investors: Redpoint China Ventures Artificial intelligence tech organization Enflame Tech brought 300 million yuan up in a round of financing, driven by Redpoint China Ventures and others included Oceanpine Capital, Yunhe Capital, Tencent Investments, Riverhead Capital, and Chinaequity Investments. The fund will be utilized in the expansion of the market, cementing of business, large scale manufacturing of products, and technical support. Enflame Tech centers around the field of AI nerve network solution.  

NeuroBlade Ltd

Amount Funded: $23 million Transaction Name: Round A funding Lead Investors: Check Point Software Technologies NeuroBlade Ltd., which is dealing in a processor for artificial intelligence applications, has finished Round A funding of $23 million. The Israeli startup intends to utilize the new financing to scale its workforce and increase its marketing endeavors to put up the first generation of its AI chip to market. Marius Nacht, co-founder of Check Point Software Technologies, drove NeuroBlade’s Series A round with the cooperation of new investor Intel Capital and existing investors StageOne Ventures and Grove Ventures.

Amount Funded: $10 million Transaction Name: Series A-II Lead Investors: Shenzhen Capital

10 Biggest Erp Software Failures Of 2011

With the year drawing to a close, one thing seems abundantly clear: There are still an awful lot of ERP and other software projects running off the rails out there.

Software project failures are no fun for anyone involved. They lead to piles of wasted money and effort, heaps of accusations and recriminations, and even to lawsuits. Here’s a look at some of the highest-profile problem projects to surface this year.

UK government scraps the £12 billion National program for IT in the NHS

In September, U.K. officials pulled the plug on what is considered to be the largest public IT project of all time, an attempt to provide electronic health records for all of the country’s citizens.

The sprawling effort was begun in 2002 but failed to produce a workable system, despite massive spending outlays that have been estimated at about £12 billion (US$18.7 billion).

The U.K.’s Major Projects Authority concluded the project was “not fit to provide the modern IT services that the NHS needs.”

“The higher they fly the farther they fall,” said Michael Krigsman, CEO of the consulting firm Asuret, which helps companies run successful IT projects. “They tried to impose a centralized solution onto just an enormous geographic and political base. It was a massive undertaking filled with political differences and technical failures, and in the end it serves as an example of what not to do.”

New York City’s CityTime project

Fallout continued this year over New York’s massive CityTime payroll system project, which has been wracked by cost overruns and a criminal probe into an alleged kickback scheme involving former employees of systems integrator SAIC and a subcontractor, TechnoDyne.

CityTime originally had a $63 million budget, but costs since skyrocketed astonishingly, with total estimates reportedly reaching $760 million.

In June, officials indicted TechnoDyne executives Reddy and Padma Allen. Others, including a number of former SAIC employees, have also been charged.

New York officials are seeking to recover money paid to SAIC. Earlier this month, SAIC said it had set aside a $232 million loss provision in connection with the case.

SAP project woes impact Ingram Micro’s profits — twice

In April, massive technology distributor Ingram Micro announced that problems with an SAP project in Australia had made a significant dent in its first-quarter profits.

Net income stood at $56.3 million, a drop from $70.3 million in the same quarter the previous year, Ingram Micro said at the time. The shortfall was “primarily attributable to difficulties transitioning to a new enterprise system in Australia,” it said.

Ingram Micro went on to stress that the system would provide a great deal of value and efficiency once in place. However, it also warned that its financial results may yet again be impacted by the project’s issues, a premonition that proved true.

In the second quarter, net income stood at $59.7 million, down from $67.7 million in the same period last year, Ingram Micro reported in July. However, the SAP project’s issues had been largely resolved, it said.

Montclair State University sues Oracle over a PeopleSoft project, but Oracle returns fire

In May, Montclair State University in New Jersey filed suit against Oracle, claiming the vendor had completely botched a PeopleSoft project that was supposed to replace the school’s aging legacy systems.

Because of Oracle’s alleged misdeeds, it might cost up to $20 million to finish the project, Montclair has claimed.

But Oracle quickly fired back, claiming that the problems were the school’s fault.

“When issues arose during the course of the project, it became clear that MSU’s leadership did not adequately understand the technology and the steps necessary to complete the project,” Oracle said in a court filing. “Instead of cooperating with Oracle and resolving issues through discussions and collaboration, MSU’s project leadership, motivated by their own agenda and fearful of being blamed for delays, escalated manageable differences into major disputes.”

Montclair recently filed an amended complaint that adds a wealth of detail to its claims, including an allegation that Oracle ran a “rigged” software demo during the sales process and was also guilty of extortion.

Epicor sued by customer over ERP project that turned into a ‘big mess’

Some ERP failures are bigger than others with respect to scope and cost, but they all can have a serious impact on a company’s operations.

Commercial outdoor furniture seller ParknPool took Epicor to court in late November over a “big mess” of an ERP project that it says will results in it taking a loss this year.

ParknPool was looking to move up from its QuickBooks system, which it was outgrowing, Jim Fonner, administrative manager of the Lexington, Virginia, company, told IDG News Service in a previous interview.

It chose Epicor over a Sage system because Epicor’s product seemed more tightly integrated, Fonner said. But nothing seemed to go right once the contract was signed, according to ParknPool, which has about 20 employees.

“Epicor said they could do it in seven weeks. We gave them seven months, and we got zero,” he said in the interview. “I couldn’t even look at a profit-and-loss statement. We couldn’t process orders. We were saying, ‘QuickBooks is so much better than this’ and we were paying $3,500 a year for it.”

In a previous statement, Epicor denied wrongdoing: “Our products, consulting personnel and partner performed well, all of which Epicor believes will be borne out as we defend our position in any proceedings.”

Marin County accuses SAP, Deloitte Consulting of a racketeering scheme

In February, the government of Marin County, California, sued Deloitte Consulting and SAP in federal court, claiming they had “engaged in a pattern of racketeering activity” aiming at bilking the county out of more than $20 million in connection with a failed ERP project.

Marin County had originally sued Deloitte in a lower court last year, claiming that the systems integrator had dumped inexperienced workers on the project, which led to the problems. The county has decided to rip out the SAP software and replace it with something else, in the belief that doing so would be a lower-cost option to finishing the job.

Its suit alleges that SAP and Deloitte are in violation of the federal Racketeer Influenced and Corrupt Organizations act (RICO). Under the statute, the county’s desired $35 million in damages would be tripled.

Marin County later said that SAP enticed it into joining a “Ramp-Up” early adopter program for the software suite, a move it claims ended up contributing to the project’s failure since the software was new and risky.

SAP and Deloitte have both denied any culpability. Deloitte has called Marin County’s federal suit a “frivolous” tactic and an attempt to get a more favorable legal forum for its claims, while SAP has questioned why it would want to collude with Deloitte on a project that was doomed to fail, among other defenses.

The lawsuit has yet to go to trial. A case management conference is set for Jan. 27.

Epicor customer sues after allegedly wild cost overruns

It’s common, and the cynical might say expected, for ERP projects to end up costing more than originally expected. But five times as much? That’s the claim of Whaley Foodservice Repairs of South Carolina, which sued Epicor in August.

Whaley, which sells and fixes equipment used by commercial kitchens, first started talking to Epicor about an ERP project in 2006. The system was supposed to be implemented and live in Whaley’s home office as well as a dozen branches by March 2007, but that goal was never met and the software has never worked as it was supposed to, according to Whaley’s lawsuit.

The project’s implementation topped $1 million, or more than five times the original estimate of $190,000, according to the suit. Epicor has denied wrongdoing, and says that under the terms of the companies’ agreement, Whaley still owes it more than $283,000.

Auditors: ERP software woes could cost Idaho millions

In March, an Idaho state auditor released a report that found that problems with a new system developed by Unisys for processing Medicaid claims could end up leading to the loss of millions of dollars.

It since began attempting to get that money back, but some $2 million was “at risk of not being recouped at all,” the report said.

The auditor’s report also pinpointed a potential root cause for the problems, noting that the system went live before certain testing milestones had been reached.

Lawson, CareSource Management head to court

Health care plan administrator CareSource Management Group sued Lawson Software in September, claiming that an ERP system from the company hadn’t been able to get beyond the testing phase and wasn’t the fully integrated suite Lawson promised.

The system instead was two modules, including the then-new Lawson Talent Management, according to the organization’s suit. CareSource was in fact one of the first companies to install the new application, it added.

As the project went on, severe data-transfer issues between the talent management module and a financial application occurred, to the degree that at one point CareSource had 20 open tech-support cases with Lawson, it states. CareSource is demanding at least $1.5 million in damages.

In response, Lawson acknowledged that “certain issues” occurred with the modules’ integration, but they were resolved. In addition, while the project did remain in a testing phase, CareSource “halted” it before filing the lawsuit, according to Lawson, which is seeking more than $335,000 in unpaid fees.

SAP-IBM payroll system woes fouled up nurses’ pay

Nurses in Nova Scotia reportedly suffered through at least six months of faulty paychecks this year due to problems with an SAP system project led by IBM.

After the Victorian Order of Nurses flipped the switch on the SAP system in January, some nurses got shortchanged while others got double their expected pay, said Janet Hazelton, president of the Nova Scotia Nurses Union, in a July interview.

“My concern is with the nurses that got $100 more,” Hazelton said. “They may not have noticed it. Our pay is never the same.”

SAP’s payroll software is solid technology, but tricky to implement given all the variables with worker pay, as well as the job of mapping over details from the legacy system, consultant Jarret Pazahanick previously told IDG News Service.

Most problematic SAP payroll project failures have the same characteristics, according to Pazahanick: “The common thread is junior consultants and weak testing.”

Despite the above roll-call of ERP horrors, there’s reason to be hopeful, said consultant Krigsman.

While the number of high-profile failures was about the same this year as last, Krigsman thinks the industry “is waking up to the fact that customers find this situation abhorrent and unacceptable.” And many of the major vendors are taking steps to address the problem.

But, “ERP vendors are only one step in a broader ecosystem that includes the customers and the system integrators,” Krigsman said. “Ultimately solving the problem requires coordination among these three groups, but certainly the ERP vendors should take strong leadership.”

Top 5 Outstanding Edge Computing Platforms Of 2023.

Here are 2023’s trendiest edge computing platforms for solution providers to watch for

Edge computing has grown in popularity in recent years as developments such as remote work, the Internet of Things (IoT), and augmented reality (AR/VR) have increased the requirement for connectivity at the network’s edge and the development of new applications. Edge deployments are now almost everywhere and meet a wide range of use cases across multiple verticals. Moreover, research firm International Data Corporation (IDC) projects that global enterprise and service provider spending on hardware, software, and services for edge solutions will continue to expand at a rapid pace through 2025, reaching about $274 billion. From private 5G, SASE, and IoT upstarts staking out their own edge niches to networking incumbents stepping into the game, here are 5 of the trendiest edge computing businesses software solutions we should keep an eye on this year.

Aarna Networks, which was formed in 2023, is on a mission to ease enterprise edge orchestration with private 5G and enterprise edge computing application automation software. Aarna Edge Services, the company’s SaaS platform, offers zero-touch orchestration for edge infrastructure and public clouds. Aarna Edge Services provides computation, storage, and network connectivity from the edge to the cloud. The major method of distribution for the San Jose, California-based firm is through channel partners. Aarna has raised a total of $3.5 million in investment, with the most recent funding coming in December 2023 from a seed round.

Adaptiv Networks, a supplier of smart corporate connectivity, has launched Adaptiv Enterprise Connect, a cloud-managed SD-WAN service. Enterprises may target new applications and users at the edge while delivering outstanding user experiences throughout all business cloud services and private corporate applications with this product. The private equity firm Quebec-based company, founded in 2002, focuses its solutions on a few verticals, including hospitality, healthcare, and retail. To bring its network as a service and co-managed cloud solutions to market, Adaptiv is relying on channel partners.

Aruba Networks, a subsidiary of HPE, is a prominent player in edge networking hardware, software, and services. The company introduces its Aruba Edge Services Platform (ESP), which enables businesses to accelerate digital transformation with automated network management, edge-to-cloud security, and predictive AI-powered insight. In addition to its 500/600 series Wi-Fi 6/6E access points, Aruba offers a broad array of CX switches, and customers like that these solutions can be controlled largely through its cloud-first, AI-powered administration platform, Aruba Central. Aruba’s services are being deployed for a range of use cases with the support of partners, including at the network’s edge and in places that are obtaining connectivity for the first time.

Cato Networks, a cloud networking service, specializes in SD-WAN and SASE. Cato’s SD-WAN and cloud-native security service edge technologies have been combined to provide global cloud can services that enforce access controls, fight against security threats, and prevent sensitive data loss. According to the Tel Aviv, Israel-based company, the company, which does 100 percent of its business through channel partners, hired channel powerhouse Frank Rauch as its new global channel chief earlier this month to help partners profit from the enterprise shift to cloud-native networking and security.

Facebook Updates Account Quality In The Midst Of Its Own Failures

It’s been a busy week for the Facebook Ads platform.

The delayed date (originally intended for September) of the Apple iOS14 roll-out hit, causing all kind of trouble for media buyers.

In the midst of it, Facebook updated its Account Quality dashboard, which is tucked inside Business Manager. While the feature itself is not new, it appears Facebook is enhancing to integrate Page-side information along with an upgraded user interface.

Account Quality Sections

The dashboard contains four sections:

Account Issues

Account Status Overview

Facebook Account

Business Accounts

Account Issues Section

This section gives a birds’ eye view to the accounts and assets that may have issues to address. Indicators at the top will note the number of outstanding vs. resolved issues:

Facebook’s Support for Issues

The changes began rolling out on Tuesday of this week, wreaking havoc on reporting dashboards.

Campaign-level insight into conversions and purchases seemed to vaporize overnight, and calculations were wrong or simply no longer there.

Twitter and Slack channels were flooded with frustrated media managers, who were suddenly flying blind on how to manage client’s spend.

Helpful tip:

If you are experiencing these issues, one way to fix them is to manually assign the attribution window at the Campaign level For example, here is one that used multiple attribution windows. There is no Purchase data, and notes there are multiple attributions:

Go to Columns, and choose “Compare Attribution.” Once you choose attribution length, your Campaign level data will populate for your selections:

The Path Forward

Updates like the Account Issues section would normally be a welcome step towards efficiency for higher-volume managers.

But, the timing made it mean very little. Many media managers would gladly forego those types of updates for consistent support, expectation management, and answers from their Facebook reps.

Sources say a fix for this attribution view problem is on the way, but it’s left many wondering whether the left hand is talking to the right. The optics of updating a UI to clarify account issues and disapprovals is in stark contrast to the fact Facebook Ad’s biggest issue often appears to be itself.

Best 5 Conversational Ai Uses In Healthcare

The pandemic has caused a shortage in the global healthcare workforce, including nurses and doctors. Several reforms have been suggested by countries around the globe to address this shortage. These reforms include reducing the barriers international medical graduates face while practicing, as well as easing the licensing process for physicians. The technology industry is not the only one that is changing. Conversational Artificial Intelligence, (AI), is ready to save the healthcare industry from this grave crisis.

Chatbot for Patient Engagement

The post-treatment phase can be kept engaged by patients using conversational AI in healthcare. Now we are familiar with the ways bots can help patients schedule appointments and diagnose problems. The post-treatment phase is as important if not more.

Scheduling An Appointment

Website/app visitors can access a chatbot via a messaging interface. Chatbots can make appointments according to doctor availability. Chatbots can also be programmed to communicate with CRM systems such as Salesforce or Microsoft Dynamics to track patient visits and follow-up appointments. This information can then be saved for future reference.

Also read:

Best ecommerce platform in 2023

Emergency Case Escalation

Resolving Commonly Asked Questions (FAQs)

The FAQ section is the most common component on any website. Hospitals and clinics have made this section a chatbot on their websites that answers general questions. This makes it easy for users to locate information.

Symptom Assessment


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