An Expert View from Emer Leahy, chief executive, and Dani Brunner, chief innovation officer, of PsychoGenics, on the challenges involved in tackling mental disorders.

One of the greatest burdens for society, affecting millions of people, is created by unmet mental health needs. Difficult-to-treat diseases such as bipolar disorder, depression, anxiety, and schizophrenia not only affect individuals,but also their families. According to recent statistics,

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We need a new Prozac: The demand for brain drug innovation - The Pharma Letter

Delivering exceptional customer experiences that give consumers control comes down to the right tools and good data.

Customers today demand a personalized, seamless experience throughout their preferred channels and they want to control the narrative.

By using conversational artificial intelligence (AI) along with good data and analytics, brands can provide an exceptional customer experience based on the customers previous interactions and data from current sessions.

This article will look at the ways brands can create such an experience, along with the challenges that often come about during the process.

Conversational AI that uses natural language processing (NLP), automatic speech recognition, advanced dialog management, deep learning and machine learning (ML) is advanced enough that it's likely to pass the Turing Test. This test determines if a computer program can perform well enough to fool a human into thinking they are talking to another human. As such, conversational AI provides a much more realistic experience than traditional chatbots.

Evan Macmillan, CEO of Gridspace, a contact center automation platform, told CMSWire that conversational AI was not very conversational until recently, and that previously, brands had to program hard rules for every possible customer intent and response.

Today large, pre-trained language models cannot fully understand language, but they are extremely useful tools, said Macmillan. Instead of programming rules, brands can use these models to flexibly and accurately recognize meaning and converse in a more natural, less scripted way.

AI-driven chatbots can be predictive and personalized, with more complex, fluid responses that are similar to human decision-making. These AI bots have access to a customer's previous interactions, typically through customer relationship management (CRM) software, can observe user-specific traits (location, age, mood, gender), learn conversational styles from past interactions and even take actions using tools such as robotic process automation (RPA).

Whether or not conversational AI is right for a brand depends on each brands specific use cases. Conversational AI is just a tool, sometimes the right one and sometimes not, it all depends on the job to be done, said Macmillan.

Related Article:4 Ways Conversational AI Is Improving the Customer Experience

Customer trust in AI has been improving greatly over the past few years. A Capgemini report indicated that 54% of customers have daily AI-based interactions with brands, and 49% of those customers found their interactions with AI to be trustworthy.

The trust in AI isnt limited to customers either employees trust AI too. An Oracle and Future Workplace [emailprotected] report revealed that 64% of employees would trust an AI chatbot rather than their manager, and 50% have used an AI chatbot rather than going to their manager for advice.

It also appears the majority of people enjoy having conversations with AI chatbots 65% of employees surveyed said they are optimistic, excited and grateful about potentially having AI "co-workers" and almost 25% said they have a comfortable relationship with AI at their workplace.

When used appropriately, conversational AI can be an effective tool in a brands customer service toolbox. Customers want to be in control of their own narrative and prefer to solve their problems without having to speak to a live agent, provided that their issue is relatively minor.

Anthony Chavez, founder and CEO at Codelab303, a team of digital experience designers, engineers and producers, told CMSWire that conversational AI can be seen as a digital agent, a brand representative that is no less critical than a teammate working on-site in a brick-and-mortar location.

"Digital agents, albeit the meta equivalent of a teammate in a store, can also create hospitable, memorable and efficient interactions with guests and customers," he said.

Data can be bad if it is unstructured, inaccurate, inconsistent, incomplete or contains duplications. Because data comes from a myriad of sources some of which are siloed, while others are in different formats or databases and yet others are unformatted there is no consistency, and it must be brought together in a structured, consistent way to be useful.

Good data, within the context of conversational AI, means data that supports NLP, NLU (Natural Language Understanding) and ultimately intent identification that is to say, the machine needs to understand what the user is asking, and then provide a human-like answer that the user is looking for, explained Chavez.

To turn bad data into good data, it must be cleansed. Data cleansing is the process of fixing bad data in a data set, which involves identifying any errors and then updating, fixing or removing them, which improves the quality of said data.

Jory Hunga, business development manager at iPaydayLoans, an online payday loans provider, told CMSWire that most brands usually have a very large amount of customer data stored in their CRM systems, which is comprised of past interactions, transactions and chat and call session transcripts.

However, said Hunga, a huge amount of this data often comes in an unstructured format like verbatim comments that can often prove to be difficult for human agents to sift through for insight.

Through the use of conversational AI bots, brands are able to make use of advanced machine learning to quickly analyze their databases and find links between pieces of information much faster and more accurately than any human agent ever could.

Related Article:How to Prepare Data for Ingestion and Integration

Customer analytics begins with aggregating and unifying data from all possible sources, including websites, mobile apps, email, chat, social media, customer service tickets and in-store visits. Once brands unify and structure that data, they can use it to create a holistic 360-degree view of each customer.

Using analytics to determine whether a user finds the answer theyre seeking is the key to crafting the best experiences, said Chavez. The best sets of data for this purpose are a blend of quantitative metrics and qualitative metrics; after all, every user will have a unique fundamental opinion about having a conversation with a machine.

Companies can use customer analytics to create personalized customer experiences and assist with customer service inquiries. Real-time data can help funnel live inquiries to the most appropriate agents at the beginning of the interaction with the customer. By interpreting and analyzing this data, brands can make the next best decision in the customer journey.

Ben Hookway, CEO of UK-based Relative Insight, a text analysis software provider, told CMSWire that effective and efficient analytics are critical to ensure that brands are delivering experiences that consumers want.

"Conversational AI is a great leap forward but absolutely key to success is a willingness from businesses to leverage the untapped gold mine that is unstructured text data as a whole the likes of customer reviews, call transcripts and survey open ends, said Hookway, who added that its customer data that holds the answers to new engagement strategies.

With the ongoing and ever-increasing deprecation of third-party cookies, the effective use of first-party data is more important than ever.

To engage effectively with consumers in an increasingly competitive and noisy landscape, Hookway added, businesses need to look at the major repositories of data which they already have and which they are generating all the time.

Because third-party cookies will eventually be phased out, marketers are looking for innovative ways to understand their audiences, he explained. Theres no doubt that technology-driven analysis surrounding first-party data often in the form of conversations with your customers will be fundamental to future success.

Brands today want to stand out for the exceptional customer experiences they provide, so they must be able to monitor and improve experiences on a minute-to-minute basis. This real-time personalization creates an emotionally positive connection and shows the responsiveness of the brand.

That said, there are many challenges to creating such an experience, especially for companies using disparate technologies.

Tom Summerfield, retail director at Peak, a decision intelligence company, told CMSWire that hes seen a number of businesses that have been approaching these challenges by designing and procuring point solution tooling to enable scaled personalized messaging, i.e., content management systems, customer data platforms, apps, website search and merch toolings, etc.

All of these tools contribute to the modern customer stack, said Summerfield, but they arent integrated and they dont talk to each other. Theyre optimising channels in silos and, without a connected approach, risk enhancing one at the expense of another.

A much better solution for brands interested in creating a seamless experience is a connected approach to personalization. Thats achieved by adding an agile SaaS (software as a service) layer between incumbent backend systems of record and stacks of point solutions.

Increasingly, this is an AI/ML platform that can identify trends and add a layer of intelligence into those processes, explained Summerfield, who believes that this represents the future of CRM: a central, agile intelligence layer fueling automated segmentation and product recommendations via an application programming interface (API).

On the other hand, Hookway told CMSWire that brands can layer data and compare it, allowing them to get more value out of it and gain rounder and more accurate views of audiences.

Doing this successfully, however, is not easy. First up, he said, it calls for a rethinking of the labels of what is thought of as marketing data and what is customer experience data. It also requires a mindset change; a process of true collaboration with leadership ready to grasp and encourage the opportunities that effective data mining and analysis presents. We cant rely on any one tool to give us all the answers.

Given that there are multiple open-source and licensable tools for NLP, NLU and intent prediction that exist today, the technology has become the easy part, according to Chavez. Designing the experience, however, in a way that is differentiated and competitive, will always be a more nuanced and persistent challenge for businesses because, in essence, you are designing a brain.

Related Article:What's Next for Artificial Intelligence in Customer Experience?

Once marketers have cleansed, structured and optimized customer data, AI, ML and NLP can analyze and use it to enhance the customer experience.

Conversational AI enables brands to provide customers with digital agents that know their shopping, purchasing and service details, facilitating a personalized conversation while enabling live agents to handle more complex inquiries all while allowing customers to control their own narrative.

Excerpt from:

Powering Customer Experience Through Conversational AI, Analytics and Good Data - CMSWire

The FBIs airport ambush described by Trunz was a crucial step in the pursuit by US prosecutors of JPMorgans precious metals desk, leading up to last weeks climax the conviction on 13 counts of the man who was once the most powerful figure in the gold market, the desks former global head Michael Nowak.

Watched with a mixture of fascination and horror by precious metals traders around the world, the case has shone a light on how JPMorgans traders including Nowak and the banks long-time lead gold trader Gregg Smith for years allegedly manipulated markets by placing bogus orders designed to wrongfoot other market participants, principally algorithmic traders whose high-speed activity became a major source of frustration.

Nowak has become one of the most senior bankers to be convicted in the US since the financial crisis, and faces the prospect of decades in prison, although it could be far less.

Nowaks lawyers contend Nowak wasnt a criminal mastermind and said they will continue to vindicate his rights in court. A lawyer for Smith said during closing arguments last month that his clients orders were legitimate, and there are other explanations to buy and sell futures contracts at the same time on behalf of customers.

It took three weeks in court for the government to persuade a jury of Nowak and Smiths guilt. (Jeffrey Ruffo, a salesman who was tried with them, was acquitted.)

But whispers of spoofing had hung over JPMorgans trading desk for at least a decade many years before the FBI first approached Trunz in 2018.

Alex Gerko, the head of an algorithmic trading firm, complained about Smiths activity in the gold market as early as 2012 to CME Group Inc., which owns the futures exchanges where the US alleged thousands of spoof trades took place. But Smith and Nowak continued working at the bank until 2019, when the US unsealed charges against them.

The wheels of justice are moving, slowly, Gerko tweeted last month.

At the Justice Department, the road to JPMorgan began with a decision to begin hunting down traders who made bogus offers to buy and sell commodities that they never intended to execute. The criminal fraud unit hired data consultants to go through billions of lines of trades to spot patterns of market manipulators.

As the vast quantities of data was scrutinized, there were certain traders that stood out. And they worked at JPMorgan.

With the data in hand, investigators went looking for cooperators, which they found in Trunz and his former colleague John Edmonds. Both relatively junior traders pleaded guilty to their own misconduct and agreed to testify against the desks boss.

Nowak was arrested in September 2019, sending a shock wave through the metals world, but the Covid pandemic meant it would be another three years until the trial finally took place.

In his testimony, Edmonds, whod started in an operations role at JPMorgan, described spoofing on the desk as a daily phenomenon and felt obliged to take part because it was part of the normal strategy.

The Justice Departments move against JPMorgans most senior bullion bankers was celebrated in some corners of the gold and silver markets, where investors and bloggers have long accused the bank of a large-scale scheme to manipulate prices lower. Those allegations prompted multiple investigations by the Commodity Futures Trading Commission, the most recent of which was closed in 2013 after finding no evidence of wrongdoing.

The case against Nowak and Smith made no allegations of a systematic plot to suppress prices, instead arguing that they spoofed markets over very short periods of time, and in both directions, to benefit JPMorgans most important hedge fund clients.

And while the convictions are a victory for the prosecutors, the jury rejected the governments most sweeping charges brought under the Racketeer Influenced and Corrupt Organizations Act, or RICO that the men were part of a conspiracy and that JPMorgans precious metals desk was a criminal enterprise.

At JPMorgan, Edmonds said the practice was referred to as clicking rather than spoofing, and the traders never discussed it as being illegal despite the firms own compliance policies making it plain. Trunz even spoke of a running joke involving Smith, who would click his mouse so fast to place and cancel orders that his colleagues would urge him to put ice on his fingers.

In 2012, Gerko, who is the founder of quantitative trading firm XTX Markets Ltd., complained to the CME about Smiths trading in gold futures by rapidly entering and canceling orders. The CME began an investigation, which dragged on for three years before concluding hed likely been spoofing.

It took a long time after 2010 to get consistent enforcement, Gerko said in a tweet, referring to the Dodd-Frank act in which spoofing was defined and made illegal.

After another JPMorgan trader, Michel Simonian, was fired in 2014 for spoofing, Nowak called his traders into his office to ask if theyd been doing the same, according to Edmonds. No one said anything. The incident shocked Edmonds, he said, as Nowak knew it had been going on for years.

During the trial, Nowak appeared largely impassive, his face hidden behind a Covid mask. Industry insiders described him in 2020 as introverted and brainy, and testimony during the trial painted him as a well-liked manager, who became friendly with Trunz while the two did a stint working out of JPMorgans London office.

During trial, Trunz was asked whether he liked Nowak, the former trader responded: I loved him.

However, the relationship became more complicated after Trunz was approached by authorities. When he contemplated making a deal with the government, Nowak told him not to, according to Trunz, who became audibly choked up as he gave the testimony.

Defense lawyers painted Trunz and Edmonds as unreliable proven liars who were testifying against their clients in order to avoid lengthy prison sentences.

Nowak and Smith wont be sentenced until next year. For comparison, two Deutsche Bank AG traders convicted of spoofing in 2020 were each sentenced to about a year in prison.

Last weeks conviction represents the pinnacle of the US Justice Departments crackdown on the illegal trading practice known as spoofing. So far, prosecutors have managed to convict ten traders at five different banks.

JPMorgan has already paid $920 million to settle spoofing allegations against it.

Even though the jury rejected the conspiracy and RICO charges, they will consider this a win, said Matthew Mazur, an attorney at Dechert LLP who defended one of the Deutsche Bank traders. This is probably the end of the precious metals sweep that was done, but I do think there will continue to be cases.

Even after the crackdown, some market participants say spoofing still takes place. Back when commodity futures traded in the pits, brokers had to trade face-to-face. Hiding behind a screen makes it much easier to place and pull orders at will.

We still see spoofing on a regular basis, said Eric Zuccarelli, an independent commodities trader who began working on the floor of the New York Mercantile Exchange in 1986. But back then if a person spoofed everybody would come over and punch you in the face and the floor committee would come over and fine you for being an asshole.

(By Eddie Spence, Joe Deaux and Tom Schoenberg, with assistance from Yvonne Yue Li)

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How the US toppled the world's most powerful gold trader - MINING.COM - MINING.com

Big data company Alteryx Inc. is flying high today after posting strong second-quarter results and offering a forecast for the next quarter and full year that came in ahead of expectations.

The company reported a loss before certain costs such as stock compensation of 46 cents per share on revenue of $180.6 million, up 50% from the same period last year. Wall Street had been looking for a wider loss of 60 cents per share on lower sales of just $161 million.

Alteryxs net loss widened to $106.8 million in the quarter, from $43.4 million in the same period one year earlier. Following the report, Alteryxs stock jumped more than 12% in extended trading, adding to a gain of just over 4% during the regular session.

Alteryx Chief Executive Mark Anderson (pictured) also praised the companys outstanding annual recurring revenue growth. It ended the quarter with $726.8 million in ARR, up 33% year-over-year. We are seeing business leaders increasingly prioritize democratization of data as part of their digital transformation journeys, Anderson said. Alteryx is uniquely positioned to enable businesses to automate workflows, upskill workforces and embrace advanced data analytics.

Alteryx sells extract, transform and load, or ETL, tools, which are used by companies to prepare data for analysis. The companys Alteryx Analytics Cloud platform provides baked-in connectivity to business intelligence tools such as Experian and Tableau, and features capabilities around data cleansing, data mining and geospatial analytics.

The companys offering has apparently found a receptive audience. Alteryx said it ended the quarter with 8,296 paying customers, up 12% from a year earlier. Whats more, Alteryx is squeezing more money from that customer base, with its dollar-based net expansion rate rising to 120% at the end of the quarter. The net expansion rate is a metric thats used to measure growth in annual contract value.

Looking to the third quarter, Alteryx offered an optimistic forecast that stands in contrast to the cautionary guidance of most other technology firms these days. The company said it sees third-quarter revenue of between $191 million and $194 million. That would represent revenue growth of between 55% and 57%, and is well ahead of Wall Streets forecast of $173.6 million in sales.

Alteryx also adjusted its fiscal 2023 guidance, saying it now sees sales of $770 million to $780 million for the full year, up from an earlier range of $750 to $760 million. Wall Street had forecast full-year revenue of just $734 million.

Holger Mueller of Constellation Research Inc. said Alteryx is proving to be a rare exception in a tech industry that is slowing down across the board, with stellar revenue growth of more than 50%.

However the growth came at a cost, with Alteryxs losses growing just as fast as its revenue, Mueller added. On the other hand, the company forecasts that it will be able to reduce its losses substantially in the third quarter. That will not straighten out its losses for the full year, but it shows investors that the company can eventually get its costs under control with Mark Andersons leadership.

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Fast-growing big-data company Alteryx beats expectations and its stock soars - SiliconANGLE News

Crypto mining firm Core Scientific reported its operations produced 1,221 Bitcoin (BTC)in July even as the company powered down several times in response to demand on the Texas power grid.

In a Friday announcement, Core Scientific said its month-over-month Bitcoinproduction had increased from 1,106 in June to 1,221 in July roughly 10.4%. The firm reported curtailing operations due to extreme temperatures at multiple data centers, but also increased the number of its self-mining servers and hashrate by 6%, to 109,000 and 10.9 exahashes per second (EH/s), respectively.

According to Core Scientific, the company completely powered-down its Texas data center operations on several occasions in July to support the Electric Reliability Council of Texas, or ERCOT, which controls the states power grid. The firm reported curtailing its power demands by 8,157 megawatt-hours (MWh).

Residents in many parts of Texas experienced several consecutive days of temperatures over 100 degrees Fahrenheit in July, with ERCOT havingforecast demand for electricity could have surpassed the available supply. Riot Blockchain, which also hosts mining operations in Texas, reported a 24% drop in its BTC production from June to July, from 421 to 318. According to CEO Jason Les, the firm curtailed operations by 11,717 MWh.

Related: Will the Bitcoin mining industry collapse? Analysts explain why crisis is really opportunity

Core Scientific CEO Mike Levitt said in July the firm planned to expand its data center hosting capacity by 75 MW, aiming to have a hash rate of 30 EH/s by the end of 2022. As of July 31, the company reported it held 1,205 BTC worth roughly $28 million at the time of publication, having sold more than 7,000 BTC for $167 million in June and 1,975 BTC for $44 million in July.

The mining firm plans to release its earnings report from the second quarter of 2022 on Aug. 11.

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Core Scientific increased Bitcoin production by 10% in July amid Texas power cuts - Cointelegraph