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J Phys Chem Lett. 2021 Jun 4:5494-5502. doi: 10.1021/acs.jpclett.1c01494. Online ahead of print.

ABSTRACT

SARS-CoV and SARS-CoV-2 bind to the human ACE2 receptor in practically identical conformations, although several residues of the receptor-binding domain (RBD) differ between them. Herein, we have used molecular dynamics (MD) simulations, machine learning (ML), and free-energy perturbation (FEP) calculations to elucidate the differences in binding by the two viruses. Although only subtle differences were observed from the initial MD simulations of the two RBD-ACE2 complexes, ML identified the individual residues with the most distinctive ACE2 interactions, many of which have been highlighted in previous experimental studies. FEP calculations quantified the corresponding differences in binding free energies to ACE2, and examination of MD trajectories provided structural explanations for these differences. Lastly, the energetics of emerging SARS-CoV-2 mutations were studied, showing that the affinity of the RBD for ACE2 is increased by N501Y and E484K mutations but is slightly decreased by K417N.

PMID:34086459 | DOI:10.1021/acs.jpclett.1c01494

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Machine Learning Reveals the Critical Interactions for SARS-CoV-2 Spike Protein Binding to ACE2 - DocWire News

At this years International Conference on Learning Representations (ICLR), a team of researchers from the University of Maryland presented an attack technique meant to slow down deep learning models that have been optimized for fast and sensitive operations. The attack, aptly named DeepSloth, targets adaptive deep neural networks, a range of deep learning architectures that cut down computations to speed up processing.

Recent years have seen growing interest in the security of machine learning and deep learning, and there are numerous papers and techniques on hacking and defending neural networks. But one thing made DeepSloth particularly interesting: The researchers at the University of Maryland were presenting a vulnerability in a technique they themselves had developed two years earlier.

In some ways, the story of DeepSloth illustrates the challenges that the machine learning community faces. On the one hand, many researchers and developers are racing to make deep learning available to different applications. On the other hand, their innovations cause new challenges of their own. And they need to actively seek out and address those challenges before they cause irreparable damage.

One of the biggest hurdles of deep learning the computational costs of training and running deep neural networks. Many deep learning models require huge amounts of memory and processing power, and therefore they can only run on servers that have abundant resources. This makes them unusable for applications that require all computations and data to remain on edge devices or need real-time inference and cant afford the delay caused by sending their data to a cloud server.

In the past few years, machine learning researchers have developed several techniques to make neural networks less costly. One range of optimization techniques called multi-exit architecture stops computations when a neural network reaches acceptable accuracy. Experiments show that for many inputs, you dont need to go through every layer of the neural network to reach a conclusive decision. Multi-exit neural networks save computation resources and bypass the calculations of the remaining layers when they become confident about their results.

In 2019, Yigitan Kaya, a Ph.D. student in Computer Science at the University of Maryland, developed a multi-exit technique called shallow-deep network, which could reduce the average inference cost of deep neural networks by up to 50 percent. Shallow-deep networks address the problem of overthinking, where deep neural networks start to perform unneeded computations that result in wasteful energy consumption and degrade the models performance. The shallow-deep network was accepted at the 2019 International Conference on Machine Learning (ICML).

Early-exit models are a relatively new concept, but there is a growing interest, Tudor Dumitras, Kayas research advisor and associate professor at the University of Maryland, told TechTalks. This is because deep learning models are getting more and more expensive computationally, and researchers look for ways to make them more efficient.

Dumitras has a background in cybersecurity and is also a member of the Maryland Cybersecurity Center. In the past few years, he has been engaged in research on security threats to machine learning systems. But while a lot of the work in the field focuses on adversarial attacks, Dumitras and his colleagues were interested in finding all possible attack vectors that an adversary might use against machine learning systems. Their work has spanned various fields including hardware faults, cache side-channel attacks, software bugs, and other types of attacks on neural networks.

While working on the deep-shallow network with Kaya, Dumitras and his colleagues started thinking about the harmful ways the technique might be exploited.

We then wondered if an adversary could force the system to overthink; in other words, we wanted to see if the latency and energy savings provided by early exit models like SDN are robust against attacks, he said.

Dumitras started exploring slowdown attacks on shallow-deep networks with Ionut Modoranu, then a cybersecurity research intern at the University of Maryland. When the initial work showed promising results, Kaya and Sanghyun Hong, another Ph.D. student at the University of Maryland, joined the effort. Their research eventually culminated into the DeepSloth attack.

Like adversarial attacks, DeepSloth relies on carefully crafted input that manipulates the behavior of machine learning systems. However, while classic adversarial examples force the target model to make wrong predictions, DeepSloth disrupts computations. The DeepSloth attack slows down shallow-deep networks by preventing them from making early exits and forcing them to carry out the full computations of all layers.

Slowdown attacks have the potential ofnegating the benefits ofmulti-exit architectures, Dumitras said.These architectures can halve the energy consumption of a deep neural network model at inference time, and we showed that for any input we can craft a perturbation that wipes out those savings completely.

The researchers findings show that the DeepSloth attack can reduce the efficacy of the multi-exit neural networks by 90-100 percent. In the simplest scenario, this can cause a deep learning system to bleed memory and compute resources and become inefficient at serving users.

But in some cases, it can cause more serious harm. For example, one use of multi-exit architectures involves splitting a deep learning model between two endpoints. The first few layers of the neural network can be installed on an edge location, such as a wearable or IoT device. The deeper layers of the network are deployed on a cloud server. The edge side of the deep learning model takes care of the simple inputs that can be confidently computed in the first few layers. In cases where the edge side of the model does not reach a conclusive result, it defers further computations to the cloud.

In such a setting, the DeepSloth attack would force the deep learning model to send all inferences to the cloud. Aside from the extra energy and server resources wasted, the attack could have much more destructive impact.

In a scenario typical for IoT deployments, where the model is partitioned between edge devices and the cloud, DeepSloth amplifies the latency by 1.55X, negating the benefits of model partitioning, Dumitras said. This could cause the edge device to miss critical deadlines, for instance in an elderly monitoring program that uses AI to quickly detect accidents and call for help if necessary.

While the researchers made most of their tests on deep-shallow networks, they later found that the same technique would be effective on other types of early-exit models.

As with most works on machine learning security, the researchers first assumed that an attacker has full knowledge of the target model and has unlimited computing resources to craft DeepSloth attacks. But the criticality of an attack also depends on whether it can be staged in practical settings, where the adversary has partial knowledge of the target and limited resources.

In most adversarial attacks, the attacker needs to have full access to the model itself, basically, they have an exact copy of the victim model, Kaya told TechTalks. This, of course, is not practical in many settings where the victim model is protected from outside, for example with an API like Google Vision AI.

To develop a realistic evaluation of the attacker, the researchers simulated an adversary who doesnt have full knowledge of the target deep learning model. Instead, the attacker has asurrogatemodel on which he tests and tunes the attack. The attacker thentransfers the attack to the actual target. The researchers trained surrogate models that have different neural network architectures, different training sets, and even different early-exit mechanisms.

We find that the attacker that uses a surrogate can still cause slowdowns (between 20-50%) in the victim model, Kaya said.

Such transfer attacks are much more realistic than full-knowledge attacks, Kaya said. And as long as the adversary has a reasonable surrogate model, he will be able to attack a black-box model, such as a machine learning system served through a web API.

Attacking a surrogate is effective because neural networks that perform similar tasks (e.g., object classification) tend to learn similar features (e.g., shapes, edges, colors), Kaya said.

Dumitras says DeepSloth is just the first attack that works in this threat model, and he believes more devastating slowdown attacks will be discovered. He also pointed out that, aside from multi-exit architectures, other speed optimization mechanisms are vulnerable to slowdown attacks. His research team tested DeepSloth on SkipNet, a special optimization technique for convolutional neural networks (CNN). Their findings showed that DeepSloth examples crafted for multi-exit architecture also caused slowdowns in SkipNet models.

This suggests thatthe two different mechanisms might share a deeper vulnerability, yet to be characterized rigorously, Dumitras said. I believe that slowdown attacks may become an important threat in the future.

The researchers also believe that security must be baked into the machine learning research process.

I dont think any researcher today who is doing work on machine learning is ignorant of the basic security problems. Nowadays even introductory deep learning courses include recent threat models like adversarial examples, Kaya said.

The problem, Kaya believes, has to do with adjusting incentives. Progress is measured on standardized benchmarks and whoever develops a new technique uses these benchmarks and standard metrics to evaluate their method, he said, adding that reviewers who decide on the fate of a paper also look at whether the method is evaluated according to their claims on suitable benchmarks.

Of course, when a measure becomes a target, it ceases to be a good measure, he said.

Kaya believes there should be a shift in the incentives of publications and academia. Right now, academics have a luxury or burden to make perhaps unrealistic claims about the nature of their work, he says. If machine learning researchers acknowledge that their solution will never see the light of day, their paper might be rejected. But their research might serve other purposes.

For example, adversarial training causes large utility drops, has poor scalability, and is difficult to get right, limitations that are unacceptable for many machine learning applications. But Kaya points out that adversarial training can have benefits that have been overlooked, such as steering models toward becoming more interpretable.

One of the implications of too much focus on benchmarks is that most machine learning researchers dont examine the implications of their work when applied to real-world settings and realistic settings.

Our biggest problem is that we treat machine learning security as an academic problem right now. So the problems we study and the solutions we design are also academic, Kaya says. We dont know if any real-world attacker is interested in using adversarial examples or any real-world practitioner in defending against them.

Kaya believes the machine learning community should promote and encourage research in understanding the actual adversaries of machine learning systems rather than dreaming up our own adversaries.

And finally, he says that authors of machine learning papers should be encouraged to do their homework and find ways to break their own solutions, as he and his colleagues did with the shallow-deep networks. And researchers should be explicit and clear about the limits and potential threats of their machine learning models and techniques.

If we look at the papers proposing early-exit architectures, we see theres no effort to understand security risks although they claim that these solutions are of practical value, he says. If an industry practitioner finds these papers and implements these solutions, they are not warned about what can go wrong. Although groups like ours try to expose potential problems, we are less visible to a practitioner who wants to use an early-exit model. Even including a paragraph about the potential risks involved in a solution goes a long way.

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Machine learning security needs new perspectives and incentives - TechTalks

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Adversarial machine learning, a technique that attempts to fool models with deceptive data, is a growing threat in the AI and machine learning research community. The most common reason is to cause a malfunction in a machine learning model. An adversarial attack might entail presenting a model with inaccurate or misrepresentative data as its training, or introducing maliciously designed data to deceive an already trained model.

As the U.S. National Security Commission on Artificial Intelligences 2019 interim report notes, a very small percentage of current AI research goes toward defending AI systems against adversarial efforts. Some systems already used in production could be vulnerable to attack. For example, by placing a few small stickers on the ground, researchers showed that they could cause a self-driving car to move into the opposite lane of traffic. Other studies have shown that making imperceptible changes to an image can trick a medical analysis system into classifying a benign mole as malignant, and that pieces of tape can deceive a computer vision system into wrongly classifying a stop signas a speed limit sign.

The increasing adoption of AI is likely to correlate with a rise in adversarial attacks. Its a never-ending arms race, but fortunately, effective approaches exist today to mitigate the worst of the attacks.

Attacks against AI models are often categorized along three primary axes influence on the classifier, the security violation, and their specificity and can be further subcategorized as white box or black box. In white box attacks, the attacker has access to the models parameters, while in black box attacks, the attacker has no access to these parameters.

An attack can influence the classifier i.e., the model by disrupting the model as it makes predictions, while a security violation involves supplying malicious data that gets classified as legitimate. A targeted attack attempts to allow a specific intrusion or disruption, or alternatively to create general mayhem.

Evasion attacks are the most prevalent type of attack, where data are modified to evade detection or to be classified as legitimate. Evasion doesnt involve influence over the data used to train a model, but it is comparable to the way spammers and hackers obfuscate the content of spam emails and malware. An example of evasion is image-based spam in which spam content is embedded within an attached image to evade analysis by anti-spam models. Another example is spoofing attacks against AI-powered biometric verification systems..

Poisoning, another attack type, is adversarial contamination of data. Machine learning systems are often retrained using data collected while theyre in operation, and an attacker can poison this data by injecting malicious samples that subsequently disrupt the retraining process. An adversary might input data during the training phase thats falsely labeled as harmless when its actually malicious. For example, large language models like OpenAIs GPT-3 can reveal sensitive, private information when fed certain words and phrases, research has shown.

Meanwhile, model stealing, also called model extraction, involves an adversary probing a black box machine learning system in order to either reconstruct the model or extract the data that it was trained on. This can cause issues when either the training data or the model itself is sensitive and confidential. For example, model stealing could be used to extract a proprietary stock-trading model, which the adversary could then use for their own financial gain.

Plenty of examples of adversarial attacks have been documented to date. One showed its possible to 3D-print a toy turtle with a texture that causes Googles object detection AI to classify it as a rifle, regardless of the angle from which the turtle is photographed. In another attack, a machine-tweaked image of a dog was shown to look like a cat to both computers and humans. So-called adversarial patterns on glasses or clothing have been designed to deceive facial recognition systems and license plate readers. And researchers have created adversarial audio inputs to disguise commands to intelligent assistants in benign-sounding audio.

In apaper published in April, researchers from Google and the University of California at Berkeley demonstrated that even the best forensic classifiers AI systems trained to distinguish between real and synthetic content are susceptible to adversarial attacks. Its a troubling, if not necessarily new, development for organizations attempting to productize fake media detectors, particularly considering the meteoric riseindeepfakecontent online.

One of the most infamous recent examples is Microsofts Tay, a Twitter chatbot programmed to learn to participate in conversation through interactions with other users. While Microsofts intention was that Tay would engage in casual and playful conversation, internet trolls noticed the system had insufficient filters and began feeding Tay profane and offensive tweets. The more these users engaged, the more offensive Tays tweets became, forcing Microsoft to shut the bot down just 16 hours after its launch.

As VentureBeat contributor Ben Dickson notes, recent years have seen a surge in the amount of research on adversarial attacks. In 2014, there were zero papers on adversarial machine learning submitted to the preprint server Arxiv.org, while in 2020, around 1,100 papers on adversarial examples and attacks were. Adversarial attacks and defense methods have also become a highlight of prominent conferences including NeurIPS, ICLR, DEF CON, Black Hat, and Usenix.

With the rise in interest in adversarial attacks and techniques to combat them, startups like Resistant AI are coming to the fore with products that ostensibly harden algorithms against adversaries. Beyond these new commercial solutions, emerging research holds promise for enterprises looking to invest in defenses against adversarial attacks.

One way to test machine learning models for robustness is with whats called a trojan attack, which involves modifying a model to respond to input triggers that cause it to infer an incorrect response. In an attempt to make these tests more repeatable and scalable, researchers at Johns Hopkins University developed a framework dubbed TrojAI, a set of tools that generate triggered data sets and associated models with trojans. They say that itll enable researchers to understand the effects of various data set configurations on the generated trojaned models and help to comprehensively test new trojan detection methods to harden models.

The Johns Hopkins team is far from the only one tackling the challenge of adversarial attacks in machine learning. In February, Google researchers released apaper describing a framework that either detects attacks or pressures the attackers to produce images that resemble the target class of images. Baidu, Microsoft, IBM, and Salesforce offer toolboxes Advbox, Counterfit, Adversarial Robustness Toolbox, and Robustness Gym for generating adversarial examples that can fool models in frameworks like MxNet, Keras, Facebooks PyTorch and Caffe2, Googles TensorFlow, and Baidus PaddlePaddle. And MITs Computer Science and Artificial Intelligence Laboratory recently released a tool called TextFoolerthat generates adversarial text to strengthen natural language models.

More recently, Microsoft, the nonprofit Mitre Corporation, and 11 organizations including IBM, Nvidia, Airbus, and Bosch releasedtheAdversarial ML Threat Matrix, an industry-focused open framework designed to help security analysts to detect, respond to, and remediate threats against machine learning systems. Microsoft says it worked with Mitre to build a schema that organizes the approaches malicious actors employ in subverting machine learning models, bolstering monitoring strategies around organizations mission-critical systems.

The future might bring outside-the-box approaches, including several inspired by neuroscience. For example, researchers at MIT and MIT-IBM Watson AI Lab have found that directly mapping the features of the mammalian visual cortex onto deep neural networks creates AI systems that are more robust to adversarial attacks. While adversarial AI is likely to become a never-ending arms race, these sorts of solutions instill hope that attackers wont always have the upper hand and that biological intelligence still has a lot of untapped potential.

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Adversarial attacks in machine learning: What they are and how to stop them - VentureBeat

Relogix will be the first in the industry to more accurately forecast and predict companies' real estate needs. Companies will potentially save hundreds of millions of real estate spend, year over year with this collaborative innovation between Relogix and Dr. Wills. "Relogix has a significant data set to work with, from years of collecting billions of terabytes of Corporate Real Estate data around the world," says Dr. Wills. "I'm excited to use this data and cutting-edge machine learning techniques to take spatial data research to the next level."

With the pandemic, it has become ever more difficult for companies to understand workplace demand for real estate, with everyone working from home and anywhere for the foreseeable future. As people return to the office, understanding the relationship between people and their demand for workspace is a significant challenge for workplace technology leaders in Corporate Real Estate, HR, and IT.

"We're making a significant R&D investment to further innovation around forecasting and predictive analytics for Corporate Real Estate," says Andrew Millar, Founder and CEO of Relogix. "We are excited to be working with Graham, a pre-eminent researcher in the AI field, and expect our collaboration to leverage advanced machine learning techniques to surface insights like never before."

As an outstanding data science leader for over 20 years, Wills is a disruptive innovator, who has been innovating predictive analytics and forecasting for 30 years. Hailing from IBM, Dr. Wills is a well-known researcher in the fields of spatial data exploration and time series monitoring. At IBM, Wills was the lead architect for predictive analytics and machine learning in IBM's Data and AI group, and led the development of major advances including intelligent automatic forecasting, natural language data insights, anomaly detection and key driver identification.

About Graham Wills, PhD:Graham's passion is analyzing data and designing capabilities that help others do the same with their data. His focus is on creating software systems that allow non-experts to draw conclusions safely and efficiently from predictive and machine learning models, and thus enhance the value of their data. Graham has authored over 60 publications, including a book in the Springer statistical series, and has chaired or presented at numerous international statistical and knowledge discovery conferences. His patents span visualization, spatial analysis, semantic knowledge, and associated AI domains. Graham believes that the goal of AI is to give professionals the assistance they need to make great decisions from their data, and that CRE is an ideal domain in which to introduce new AI and Machine Learning capabilities to revolutionize the marketplace.

About Andrew Millar, CEO:Andrew's mission is to turn data into valuable outcomes. With over 20 years as a corporate real estate solutions and insights provider, Relogix founder and CRE veteran, Andrew Millar, recognized the need for technology in the CRE industry. He founded Relogix out of a need to create solutions to help organizations evolve their workspace and get high quality data to drive strategic decision making. Andrew believes that the key to evolving workspace and strategic planning lies in data science. Just like the workplace, data science is progressive: it is a journey of perpetual discovery, refinement, and adaptation. Andrew has since created proprietary sensor technology with the needs of corporate real estate in mind technology created for CRE professionals by CRE professionals.

About Relogix:Trusted by top Corporate Real Estate professionals who need to make data-driven business decisions to inform their real estate strategy and measure impact. Our flexible workplace insights platform and state-of-the-art IoT occupancy sensors are proven to transform the workplace experience. We're always looking for the next innovation in workplace technology, leveraging two decades of CRE and analytics expertise to help our clients understand and optimize their global real estate portfolios.

SOURCE Relogix Inc.

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Relogix Announces Collaboration with Dr. Graham Wills, Predictive Analytics and Machine Learning Expert, To Better Predict Office Space Needs -...

By Matthew Hodgson, CEO, Mosaic Smart Data

In our last article we looked at how productivity is one of the core benefits of a bank gaining control over its data and analysing it more effectively. But once a bank has gained this control and insight, how does it go a step further by augmenting sales teams with AI and machine learning tools that can digest large data sets and alert them to the needs of their clients?

Traditionally, banks have always held an information advantage over their clients in fact their business models have been specifically designed to leverage the market information to which they have access and transform it into value enhancing insight.

But in recent years, this advantage has been slowly chipped away at as the markets have become increasingly electronic and the buy-side has upped its game in terms of the data to which it has access, and its ability to analyse large amounts of it. As market and price transparency has increased, one of the core competitive advantages of a banks sales desk has been eroded.

Against this shifting backdrop, banks are starting to realise the potential power of innovative machine learning and AI tools in helping them to upskill and maintain their competitiveness in the sales arena. There is a dawning realisation that backward looking BI analysis is not fit for purpose in driving business forward, especially in this era of utilising AI to squeeze every possible efficiency and productivity from the resources at hand.

Some are now starting to deploy these technologies to enable predictive and prescriptive analytics, as well as connecting systems to prompt them as to the next best action for their clients. By absorbing information that might otherwise be missed, AI delivers the analysis to drive new sales engagement with clients and by delivering those insights at the optimum time.

Investment and adoption at scale is expected to increase significantly over the coming years. This comes as no surprise when you consider it has been estimated by McKinsey that AI can potentially unlock $1 trillion of incremental value for banks[1]. These tools can be thought of as a GPS for the sales desk those banks without it will struggle to compete against more forward-leaning firms who are empowering their employees with the most advanced digital tools.

The evolving role of the salesperson

According to a recent report from PwC, almost 80% of banking and capital markets CEOs see skills shortages as a threat to their growth prospects.[2]This is because, quite simply, banks havent managed to keep pace with the changing manner in which their clients want to interact with them.

While no one is suggesting robots will completely replace salespeople any time in the near future, there are certain skills that can be enhanced when man and machine work together in tandem. One of the main skills that clients increasingly demand from banks is a more customised and tailored experience, which in turn drives a more intimate and refined relationship.

In addition, as electronification continues to grow, sales teams tend to manage a larger pool of clients across asset classes. Clients expect salespeople to provide a seamless service in multiple asset classes and have a global view of flows across the organisation.

Data therefore needs to be aggregated from across the organisation and made available to salespeople in one consolidated and comprehensive view so they can, for example, alert clients about new investment opportunities as they unfold no matter the asset class.

Bridging the skills gap

In recent years, a growing number of large investment banks have launched ambitious projects to apply AI and machine learning techniques to previously unexplored data sources, in order to bridge the skills gap and improve how they sell to clients. A recent survey found that 75% of banks with over $100 billion in assets are currently implementing AI strategies.[3]

Using the right technology, a combination of internal transaction data, external data feeds and unstructured data sources such as newsfeeds, can be standardised and aggregated into one holistic view. AI-powered advisory tools can then be applied to help banks anticipate client activity in order to build inventory for expected demand, identify unique and unforeseen market opportunities, extract timely information from news and websites, and alert sales based on market triggers.

Using AI and machine learning you can, for example, see which customers are likely to defect and move their business elsewhere, and therefore up your defensive measures. After all, it is much more expensive to acquire a new customer than it is to maintain an existing one. You can also become more responsive and relevant to clients, because you are able to see what customer activity you anticipate on a particular day and then serve that customer with the appropriate inventory.

This technology has been leveraged over the last number of years to improve the service high-street banks deliver to retail customers. However, within investments banks the benefits of these same tools are beneficial to sales desks covering all types of clients including corporates, hedge funds, asset managers, insurers, pension funds, central banks and even internal clients.

Some banks are also exploring the use of natural language generation (NLG). This is a software process that automatically transforms data into a written narrative, making lightning-fast generation of expert business intelligence and reporting a reality in todays financial markets.

NLG can generate intuitive prose that reads as if it were written by the best quant in the house at the click of a button, equipping sales teams with the collateral they need to offer up the most appropriate trading opportunities to their clients. These reports can even be prepared with enough variance and nuance in language and style to keep the copy fresh and engaging to the reader. This power of NLG is driving enormous time saving benefits across the organisation by taking laborious daily tasks and automating them at the click of a button.

Becoming AI-first

These are just a handful of examples of how AI and machine learning can help sales desks deepen customer relationships, provide personalised insights and recommendations, and, ultimately, turn the profit dial in their favour.

Banks that fail to make AI central to their core strategy and operationsoften referred to as becoming AI-firstwill risk being overtaken by competition and deserted by their customers in the coming years.

The current operating environment is both uncertain and challenging for investment banks, but a carefully planned programme that builds on cutting-edge data analytics and AI technology holds the key to driving growth and delivering the modern, information-driven trading experience that clients demand.

After all, its typically during periods of stress where relationships are forged. As a bank, if youre able to guide a client through the fog of confusion, you will likely have a relationship for life and AI and machine learning can assist in facilitating this.

But dont just take our word for it. A client recently told us that since deploying AI technology across the front desk, their sales team had made 20% more calls, had 22% longer conversations with clients, and this had resulted in significantly more volume seen and executed. If youre a salesperson known to have the best information, the client will call you first. Its that simple.

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Man, meet machine: the role of AI and machine learning in the modern sales desk | Global Banking & Finance Review - Global Banking And Finance...