Virtually all businesses today rely on digital technology to drive major operations, including upstream and downstream supply, sales and marketing, recruitment and onboarding, and internal communication. This is a partial list, but a telling one – symbolic of the reasoning behind the statement that “every company is now a technology company.”

The extent to which organizations use technology in the 21st century has a profound impact on growth, profitability, and overall success. As leaders and technologists increasingly work within the same domains, here are the five best practices that should be observed to leverage the skills of both and drive businesses forward:

1. Do not lead with technology in mind.

Technology’s starting point for driving ongoing business success is helping define the complex business problems in need of solutions and employing strategies that will most effectively support growth. The technology itself often plays a big part, but it should never be the primary focus.

Instead, leaders should always put stakeholders first. Customers, investors, and employees are the core building blocks of business, and fulfilling their needs (both stated and unstated) is the main driver of long-term growth and success.

2. Do not allow technology to remain siloed.

Technology has historically been viewed as a back-end function, receiving instructions and carrying them out independently. However, the divide between strategy and technology can be viewed as a fundamental misunderstanding of how to operate in the information age: Digital transformation is not only about implementing more and better technology but also about overlaying all traditional business processes with the power of those technologies offer.

This means it’s vital for executives to drive a genuine paradigm shift where data and software are concerned. Allowing technology to function as a disconnected department or entity limits its vast potential, along with that of the organization it is meant to serve.

3. Use data as the fulcrum for strategic advantage.

Seismic shifts in technology (artificial intelligence, machine learning, and the advent of microprocessing, to name a few) led to a tidal wave of data. Companies in many industries now access billions of unique data points on a daily basis. All of this information can provide insight regarding many important questions, including:

• How are customers interacting with products and perceiving brands?
• Which customer touchpoints are most valuable?
• Which moments are most critical in the customer journey?
• How well are products and services performing throughout their lifecycle?
• What shifts in the market are happening or are imminent, and how might they affect operations?

Answers to questions like these are an integral part of an evolving business strategy. The value of information is compounded by the speed at which it can be processed, and leveraging fast data is now not only a possibility for most organizations but a necessity to stay competitive.

4. Make technology the center of innovation.

Companies that lead in innovation often lead in the marketplace as well. Digital platforms and tools not only allow companies to ask smarter questions but also facilitate fast and easy implementation of ad hoc solutions testing.

When technology was hardware-focused, research and development was an expensive, time-consuming investment. Today, largely due to a shift to cloud infrastructure, it is a quick and highly dynamic operation with the ability to set companies apart in their sector. Multiple solutions can be experimented with simultaneously, providing deep answer sets and generating additional data in the process.

5. Prioritize the problems that arise at the juncture of technology and business.

In many cases, technology alone can do little to alter the trajectory of an organization.

However, it exponentially increases the value of traditional business knowledge by giving leaders the ability to ask and answer more complex questions.

“Business as usual” in most industries today is technologically enhanced and massively empowered when compared to just two decades ago. And yet, much of technology’s potential remains latent. Leaders who wish to activate it should start by understanding that the bottleneck is still fundamentally human. Internal barriers need to be broken down, and clear communication lines must be established between historically siloed teams. When this takes place, the true power of technology’s ability to innovate when it comes to customer experience and drive business growth can be unleashed – and the organizations that implement it will lead us into the next era of business.

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Companies should develop a data science strategy to drive business intelligence.

Business intelligence is not a luxury anymore but a necessity today. The rapid adoption of disruptive technologies has enabled companies to enhance growth and agility. Data fuels businesses in the current scenario and it enables companies to gain intelligent insights. Hence, data science is an important part of regular business processes. Different companies use different methods to optimize data and make better decisions. This is often called a data science strategy. The significance of a data science strategy is immense in driving growth and staying close to the customers.

Why A Strategy?

Creating an effective data science strategy is not as simple as it sounds. Our world is getting smarter every day and businesses need to stay on the competitive edge to achieve success. A data science strategy or data strategy will enable the company to reach the right data, metrics, and data resources with ease and better accessibility. Developing a strategy will need a company to first define its goals, it can be a larger and measurable goal like generating more revenue. The next step is to find the right data resources suitable to the business goal. The executives need to clearly define the questions that they want the data insights to answer so that the company does not end up following unuseful and wrong data. A clearly articulated business strategy can ease the process of developing a data science strategy.

Building A Strategy

As mentioned above, the initial step would be to define measurable goals and find the right data resources. Next is identifying the project infrastructures by understanding which technologies to use, should everything be developed internally or outsourced, etc. The company should also decide the data storage platform and the desired form in which you would like to get insights like visuals, charts, reports, and more. Building a data science strategy also involves deciding the algorithms and technological models that should be used. This includes AI, machine learning, statistical inference, and making clear if these algorithms need to be transparent and explainable.

Another most important step is constituting a data science team. Hiring data scientists can be a bit difficult today as the role is in high demand. The company should analyze if it is going to build an in-house data science team or hire experts from outside. Collecting and storing data will create regulatory obligations that need to be met. Companies should consider data governance as an essential component to avoid data becoming a risk and liability. For this, the data science strategy should include compliance, security measures, and privacy policies in place.

Once all these steps are accomplished, a company can then use data analytics to process the huge amount of data and get actionable insights through disruptive technologies. Data and analytics are the crucial part of business intelligence today and intelligent insights are the only way to understand the audience better and create personalized services.

Data science and machine learning go hand in hand. Machine learning, a subset of AI, is an effective and widely used tool to deliver data analysis and insights. Machine learning models can be fed with data and this will enable the machines to learn from these data and improve from past patterns and risk behaviors. Ensuring data quality often becomes a challenge and integrating machine learning into the data strategy can help overcome it. Machine learning can accurately detect errors with minimal human intervention. Machine learning and data science strategy intersect and this is the current business intelligence scenario. Hence, companies should have a data strategy in place to enhance growth and efficiency.

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STUTTGART, Germany — NATO and its member nations have formally agreed upon how the alliance should target and coordinate investments in emerging and disruptive technology, or EDT, with plans to release artificial intelligence and data strategies by the summer of 2021.

In recent years the alliance has publicly declared its need to focus on so-called EDTs, and identified seven science and technology areas that are of direct interest. Now, the NATO enterprise and representatives of its 30 member states have endorsed a strategy that shows how the alliance can both foster these technologies — through stronger relationships with innovation hubs and specific funding mechanisms — and protect EDT investments from outside influence and export issues.

NATO will eventually develop individual strategies for each of the seven science and technology areas — artificial intelligence, data and computing, autonomy, quantum-enabled technologies, biotechnology, hypersonic technology, and space. But for the near future, the priority is AI and data, said David van Weel, NATO’s assistant secretary general for emerging security challenges.

The alliance expects to release an artificial intelligence strategy by this summer, he told Defense News in an exclusive March 4 interview. This effort comes on the heels of the U.S. Congress backing the creation of a national AI strategy in January as part of the country’s annual defense authorization bill.

NATO would do well to have its own AI and data policy strategies in place, van Weel said. He expects the strategies to include NATO’s plans for setting interoperability and technology standards across all EDT domains, and provide guidance on both principles for responsible use of AI-enabled platforms and export control mechanisms.

“It’s basically enabling the organization to then be able to start incorporating AI into military requirements for NATO itself, but also for our allies,” van Weel said. “Data and AI are the first [EDTs] that we will pick up with speed and we’ll deliver on this year.”

The EDT implementation strategy was endorsed during the alliance’s annual meeting of defense ministers in February, and followed the establishment of an EDT road map during the 2019 alliance summit in London. The defense leaders of NATO’s members along with their counterparts in Sweden, Finland and the European Union met virtually for the 2021 ministerial.

The overarching goal of the strategy was to create the conditions for continued interoperability across the alliance as it tackles “a whole new field” of technologies on the horizon. “One of the big challenges when we go into this new phase of disruptive technologies is how do you keep all allies on the same hymn sheet when it comes down to communicating with each other, using the same technology, being interoperable,” van Weel said. “So that’s a big part [of the strategy] and a big role for NATO to play.”

The post Artificial intelligence leads NATO’s new strategy for emerging and disruptive tech appeared first on Artificial Intelligence.

Speaking at DIGIT’s inaugural MarTech Virtual Summit, Daniel Winterstein, CTO at Good-Loop, discussed the ‘evolution’ of creative AI, and how the tech can aid your marketing strategy.

In recent years we have seen a series of breakthroughs in the capabilities of artificial intelligence (AI) technology.

We are now adopting AI tech across industries worldwide and using it to power thousands of products in our lives, including computers, cars and even our kettles.

Business leaders, too, are beginning to see the value of AI integration within their organisations.

Research carried out in 2019 by Accenture revealed that that four out of five UK executives understand the need to scale AI across their firm to survive and remain competitive.

But how can AI be utilised for marketing purposes? In his talk at DIGIT’s inaugural MarTech Virtual Summit, Good-Loop CTO Daniel Winterstein covered a “realistic look” at what AI can do today in advertising.

Touching on the power of “unlocking smart personalisation and dynamic creation,” Winterstein also discussed the problems with AI and the creation of ‘bland’ advertising.

AI can allow marketers to create ‘on the fly’ and personalise campaigns for users. “What could possibly go wrong with that?” Winterstein asked.

What can AI do?

Using examples of historic versus current marketing logos and products, Winterstein suggested that the move to more modern advertising means that marketing campaigns and products have “lost a lot of identity”.

Winterstein identified that identity loss has been happening across advertising and marketing, driven by market testing and a push towards the mainstream; something which could be now accelerated by AI.

But to understand how AI can impact your marketing, you must first understand what AI itself is. Winterstein said that AI can cover a lot of areas, and means different things to different people.

“There are deep learning neural networks behind much of the cutting-edge AI. A lot of practical AI, though, is often driven by simplest things like flowcharts,” he said.

AI can also mean automation. Tasks that were once carried out in factories are now being done in offices around email and signup flows. However, Winterstein said that AI might not always be as it seems.

He commented: “It can sometimes mean people in disguise. More than once, a service that has been presented as AI has actually been powered at the back end by humans. And it can be a buzzword. Sometimes something masquerading as AI is just common software wearing shiny clothes.”

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Despite these potential issues, he said that all the tasks that AI can carry out are valuable and “have their place in our businesses”.

Current tools using AI software to generate images are useful for marketing purposes. While searching for or creating an image to use for marketing, you would have to consider copyright and privacy concerns. In the case of AI-generated images, this is not the case.

Winterstein said: “These tools have the ability for you to set up photos that have never existed. You can do Photoshop from your office computer [to produce these images].

“This technology is getting better year on year; it’s hard to say where the limits are. I am not sure if there are limits.”

Don’t be bland, and consider the ethics

However, Winterstein brought the talk back to identity loss, and the more common issue of marketers simply using AI to create blank advertising simply because it ‘works’.

“When people start looking at deploying AI in brand-safe spaces, there will be this push for bland, because it’s safe,” he said.

“Just because we have these tools which can create and personalise, it doesn’t mean that they are always giving value. Personalisation often isn’t actually that personal.”

So how can we avoid the creation of bland marketing campaigns? Winterstein suggests that, by injecting purpose and identity into the heart of your campaign, purpose-driven AI can deliver real results.

However, Winterstein also raised an important point on the ethical issues surrounding AI, mainly that every AI project should implement an ethics plan. Failing to do so can have a hugely negative impact on any project.

“If you roll out an AI without thinking about what you’re doing and what can go wrong, then it is all too easy to have unintended consequences, like systems which are sexist or systems that are racist,” he said.

Winterstein’s talk ended with a comment from AI software on the importance of ethics and the consideration of the purpose of your marketing campaign: “Design is a way to communicate your values to your audience. In order to do that, you have to really know who your audience is and what they care about.

“Brand purpose is the single most important way to drive the identity of your brand. The best brands are the ones that aren’t just trying to win customers but are also trying to make the world a better place.

“AI projects should be purposeful by design. One of the most important things to remember about projects that involve AI is that they should be inherently useful and help you achieve something that you couldn’t otherwise achieve without.”

The post MarTech 2021 | Enhancing a Marketing Strategy with Artificial Intelligence appeared first on Artificial Intelligence.

Simulating a tabletop game can be done for several reasons: to play the game digitally, to create computer opponent(s), or to prove someone wrong. In [Everett]’s case, he used Python to prove which adult was right about basic strategy in a children’s game.

[Everett]’s 5-year-old loves a simple game called Hoot Owl Hoot! in which players cooperatively work to move owls along a track to the safety of a nest. Player pieces move on spaces according to the matching colors drawn from a deck of cards. If a space is already occupied, a piece may jump ahead to the next available spot. The game has a bit more to it than that, but those are the important parts. After a few games, the adults in the room found themselves disagreeing about which strategy was optimal in this simple game.

It seemed to [Everett] that it was best to move pieces in the rear, keeping player pieces grouped together and maximizing the chance of free moves gained by jumping over occupied spaces. [Everett]’s wife countered that a “longest move” strategy was best, and one should always select whichever piece would benefit the most (i.e. move the furthest distance) from any given move. Which approach wins games in the fewest moves? This small Python script simulates the game enough to iteratively determine that the two strategies are quite close in results, but the “longest move” strategy does ultimately come out on top.

As far as simulations go, it’s no Tamagotchi Singularity and [Everett] admits that the simulation isn’t a completely accurate one. But since its only purpose is to compare whether “no stragglers” or “longest move” wins in fewer moves, shortcuts like using random color generation in place of drawing the colors from a deck shouldn’t make a big difference. Or would it? Regardless, we can agree that board games can be fitting metaphors for the human condition.

The post PYTHON SETTLES BET ABOUT BEST STRATEGY IN CHILDREN’S BOARD GAME appeared first on Artificial Intelligence.

An effective microservices logging strategy can hinge on the size and scale of the system in question. For example, a microservices-oriented architecture composed of 20 microservices is less of a logging burden when compared to one composed of 200 microservices.

Developers who hope to introduce a successful microservices logging strategy need to craft a plan that lays out where the logging takes place and how it affects other areas of the system. Typical logging can stress a system in three ways: I/O, storage and analytic computation on the CPU.

Before a team deploys a microservices logging strategy, it must consider the potential stresses on the system, and what it might mean to additional development. Let’s examine ways to alleviate system stresses and some alternatives to traditional microservices logging strategies.

Log data on the machine

The easiest way to introduce logging on a microservices-oriented architecture is to have each microservice collect and store its logging data on the machine where it runs. This is probably the easiest and simplest approach to logging, but it’s also one fraught with danger.

Storage on a local machine significantly reduces I/O latency because all the activity takes place at a singular location. There are few, if any, trips out to the network. While logging data on the local machine helps improve performance, there is a tradeoff.

Increased storage places a significantly higher burden on the host machine’s CPU. Higher levels of activity result in more logging, which in turn creates more log data stored in the system and raises CPU utilization levels. A host machine can be maxed out in no time under this scenario.

Luckily, there are alternatives to this microservices logging strategy.

Logging services

A logging service can help alleviate concerns of CPU utilization and reduced I/O latency.

A logging system’s main benefit is that the storage and work is moved off the system and onto third-party resources. All the microservice needs to do is take a trip out to the network to send log entries.

While this doesn’t seem like a big deal on smaller architectures, it can be problematic if there are 200 microservices that run in a high-availability, multi-replica environment. In a situation like this, many trips to the network from many origins can cause a bottleneck and bring other network communication to a grinding halt.

In this case, there is another alternative that developer teams should consider.

The collector strategy

A collector strategy essentially shifts how log entries are sent in and out of the network.

Instead of sending each log entry out to a logging service, they are sent to a central collector that resides on a machine elsewhere. In most cases this machine is at least in the same data center of the microservices-oriented architecture. In a best-case scenario, the machine is located on the same data center rack that hosts the other microservices components.

Cloud-hosted microservices users will have to consult with their provider on identifying the best place to host the collector.

The collector does as its name implies. It collects all the log entries emitted from the architecture and then forwards the entries onto a logging service at a prescribed interval. Once the entries arrive, the collector flushes the old log entries from the system to backup storage.

One major benefit to this microservices logging strategy is that the collector absorbs the network latency incurred when it sends the log entries onto the logging service. Also, because the collector is close to the other components of the microservices-oriented architecture, it reduces latency between the architecture and the collector.

However, there are still risks associated with a log collector. For example, if the central log collector fails, all logging activity comes to a standstill.

So, how can developers avoid this risk?

Collector clusters

Developers can create a cluster of collectors that resides behind a common load-balancer to alleviate failed central log collector concerns.

A benefit of the load-balanced log collector strategy is that if one collector fails, the others will remain operational and allow logging to continue. But there is a tradeoff.

This strategy requires a team to support a set of collectors on their network, and in turn, adds more expenses related to the increase of virtual machines. Also, the logging environment becomes more complex and requires more legwork from other microservices in the environment.

Overall, the main crux of the microservices logging conundrum is scale. If you run a smaller architecture without a lot of logging, keep the logging activity on the same microservices elements. However, if you run an architecture with a lot of microservices, a more sophisticated logging strategy makes more sense, despite some potential drawbacks with cost and storage.

The post What makes an effective microservices logging strategy? appeared first on Artificial Intelligence.

Data centers and disaster recovery are still structured around traditional transaction systems, but more companies are beginning to use big data in mission-critical ways. It’s time for IT to define DR and “mission-critical” status for big data. What are the critical ingredients that support both, and what should companies be doing now?

First, let’s look at some examples of how big data became mission-critical data.

At the Hospital for Sick Children in Toronto, Project Artemis focuses on a real-time data collection and analysis system that monitors babies’ heart rates and issues real-time alerts when a heartbeat indicates the possibility of neonatal sepsis, which can result in death. The alerts are sent to nurses so they can immediately intervene. The technique reduces the likelihood of infant deaths.

In manufacturing, real-time IoT data reported from production lines immediately informs manufacturers when an assembly line or a piece of equipment is in danger of failing. The alerts trigger maintenance so that production lines stay up and continue to run 24/7. The average cost of a single downtime incident in manufacturing is $17,000, and a single manufacturer can experience as many as 800 downtime incidents per year. From cost and competitive standpoints, big data IoT reporting in manufacturing is mission critical.

Across industries, whether it is healthcare, financial services, agriculture, logistics or life sciences, big data has joined traditional structured data as a mission-critical element.

Strategies for mission-critical big data

Since organizations are only now beginning to classify big data applications as mission critical, most are in early stages of developing IT strategies supporting the mission criticality of big data.

As companies work through these issues, here are some major questions that IT strategies around mission-critical big data should address:

1. What are the major mission-critical big data apps in the business?

Do you depend on streamed IoT to inform management about the environmental safety and locations of sensitive cargo you are transporting? Or do you use big data for in-field utility and equipment inspections conducted by drones?

If the company is depending on these functions to replace daily, operational tasks that were formerly performed manually, or if revenue, cost and/or safety factors are impacted, the big data applications should be identified as mission critical and presented as such. Upper management and the board of directors need to also understand the significance of maintaining the operations of these systems.

Mission-critical systems must stay operational no matter what, and there must be system backups, IT staff, and management/board support to sustain them.

2. Are there backups for data and operations?

In some big data processing systems like Hadoop, there is built-in processing and data failover, but in other cases it might be necessary to establish big data backup methods in the data center or in the cloud.

For instance, if the logistics tracking system you depend on for your truck fleet suddenly fails, what happens? Are you using a vendor transportation management system in which the vendor provides failover? Or do you have backup data and processing capability on premises or in the cloud that can take over?

What about your personnel? If a key contributor on your big data staff is unavailable, do you have a backup option for that person?

These are questions that have long been answered for traditional transaction systems, and that now must be answered for big data.

3. How robust is your security?

Big data systems that rely on IoT sensors, devices and appliances that are used far from headquarters or the data center are often overseen by end users who are not as cognizant of security best practices as IT teams.

There is always the potential for bad actors to compromise big data systems in the same way that they attempt to breach standard systems of record. Additionally, out-of-the box IoT appliances may come with vendor security presets that are too loose to meet your security requirements.

A security evaluation should be made for any big data system that you classify as mission critical. Often, an initial review by an outside audit firm can help you identify potential security holes.

4. Can you trust your data and your algorithms?

Many of us remember the Google Flu Trends failure to identify the peak of the flu season in 2013 by 140%.

Google is hardly alone in big data “misses.” Many companies inaccurately design their data models and the algorithms that operate on big data. There are also cases in which data comes into systems without being adequately “cleaned” (i.e., screened for accuracy and relevancy, and normalized so it can be aggregated with data from other systems).

Because big data uses iterative data models that can change from day to day, it’s also important to assure that the latest versions of data, data models and algorithms are in production and available to users.

To support these functions in mission-critical big data systems, IT should develop policies and procedures for tracking data, data model and algorithm versions, and for assuring that the latest versions of all are in place.

5. Are big data systems in your disaster recovery and failover plan?

Many organizations have yet to incorporate mission-critical big data systems into their formal disaster recovery and business continuation plans. They should if these systems are being used to run and manage critical operations in the company.

This is a good time for CIOs and big data leaders to review DR plans and fill in sections that may be missing for big data systems that are listed as being mission critical. It’s equally important to communicate the additions of these systems to management and the board –and to arrange for periodic DR and failover testing.

The post How to Approach Your Mission-Critical Big Data Strategy appeared first on Artificial Intelligence.

Python backdoor attacks are increasingly common. Iran, for example, used a MechaFlounder Python backdoor attack against Turkey last year. Scripting attacks are nearly as common as malware-based attacks in the United States and, according to the most recent Crowdstrike Global Threat Report, scripting is the most common attack vector in the EMEA region.

Python’s growing popularity among attackers shouldn’t come as a surprise. Python is a simple but powerful programming language. With very little effort, a hacker can create a script of less than 100 lines that establishes persistence, so that even if you kill the process, it will start itself back up, establish a backdoor, obfuscate communications both internally and with external servers and set up command and control links. And if an attacker doesn’t want to write the code, that’s no problem either. Python backdoor scripts are easy to find – a simple GitHub search turns up more than 200.

Scripting attacks are favored by cybercriminals and nation states because they are hard to detect by endpoint detection and response (EDR) systems. Python is heavily used by admins, so malicious Python traffic looks exactly like the traffic produced by day-to-day network management tools.

It’s also fairly easy to get these malevolent scripts onto targeted networks. Simply include a malicious script in a commonly used library, change the file name by a single character and, undoubtedly, someone will use it by mistake or include it as a dependency in some other library. That’s particularly insidious, given how enormous the list of dependencies can be in many libraries.

By adding a bit of social engineering, attackers can successfully compromise specific targets. If an attacker knows the StackOverflow usernames of some of the admins at their targeted organization, he or she can respond to a question with ready-to-copy Python code that looks completely benign. This works because many of us have been “trained” by software companies to copy and paste code to deploy their software. Everyone knows it isn’t safe, but admins are often pressed for time and do it anyway.

Anatomy of a Python backdoor attack

Now, let’s imagine a Python backdoor has established itself on your network. How will the attack play out?

First, it will probably try to establish persistence. There are many ways to do this, but one of the easiest is to establish a crontab that restarts the script, even if it’s killed. To stop the process permanently, you’ll need to kill it and the crontab in the right sequence at the right time. Then it will make a connection to an external server to establish command and control, obfuscating communications so they look normal, which is relatively easy to do since its traffic already resembles that of ordinary day-to-day operations.

At this point, the script can do pretty much anything an admin can do. Scripting attacks are often used as the point of the spear for multi-layered attacks, in which the script downloads malware and installs it throughout the environment.

Fighting back against Python backdoors

Scripting attacks often bypass traditional perimeter and EDR defenses. Firewalls, for example, use approved network addresses to determine whether traffic is “safe,” but it can’t verify exactly what is communicating on either end. As a result, scripts can easily piggyback on approved firewall rules. As for EDR, traffic from malicious scripts is very similar to that produced by common admin tools. There’s no clear signature for EDR defenses to detect.

The most efficient way to protect against scripting attacks is to adopt an identity-based zero trust approach. In a software identity-based approach, policies are not based on network addresses, but rather on a unique identity for each workload. These identities are based on dozens of immutable properties of the device, software or script, such as a SHA-256 hash of the binary, the UUID of the bios or a cryptographic hash of a script.

Any approach that’s based on network addresses cannot adequately protect the environment. Network addresses change frequently, especially in autoscaling environments such as the cloud or containers, and as mentioned earlier, attackers can piggyback on approved policies to move laterally.

With a software and machine identity-based approach, IT can create policies that explicitly state which devices, software and scripts are allowed to communicate with one another — all other traffic is blocked by default. As a result, malicious scripts would be automatically blocked from establishing backdoors, deploying malware or communicating with sensitive assets.

Scripts are rapidly becoming the primary vector for bad actors to compromise enterprise networks. By establishing and enforcing zero trust based on identity, enterprises can shut them down before they have a chance to establish themselves in the environment.

The post Python backdoor attacks and how to prevent them appeared first on Artificial Intelligence.

Machine learning is the holy grail of data analysis, but unfortunately, that holy grail oftentimes requires a PhD in Computer Science just to get started. Despite the incredible attention that machine learning and artificial intelligence get from the press, the reality is that there is a massive gap between the needs of companies to solve business challenges and the availability of talent for building incisive models.

YC-backed Intersect Labs is looking to solve that gap by making machine learning much more widely accessible to the business analyst community. Through its platform, which is being launched fully publicly, business analysts can upload their data, and Intersect will automatically identify the right machine learning models to apply to the dataset and optimize the parameters of those models.

The company was founded by Ankit Gordhandas and Aaron Fried in August of last year. In his previous job, Gordhandas deployed machine learning models to customers and started working on a tool that would speed up his work. “I actually realized I could build a version of the tool that was a little more advanced,” he said, and that work ultimately led to the foundation of Intersect Labs. He linked up with Fried in October, and the two have been working on the platform since.

Intersect’s goal is to move analysts from purely retrospective analysis to creating models that can predictively determine business strategy. “People who live in SQL and Excel, they are really good at pulling the data of the past, but we are giving them the superpower of seeing the future,” Gordhandas explained. “All you need is your historical data, upload to our platform, and answer two questions.”

Those questions essentially ask what the model should predict (the outcome variable). From there, Intersect begins by cleaning up the data and ensuring that the various columns are properly scaled for data analysis. Then, the platform begins constructing a range of machine learning models and evaluating their performance against the target output. Once an ideal model is identified, customers can integrate it into their other systems through a REST-style API.

What’s interesting here is that Intersect can get better and better at identifying models over time based on the increasing diversity of datasets that it gets access to. Plus, as researchers identify new models or ways to tune them, the platform can potentially proactively improve the models it had previously identified for its customers, ensuring that they stay at the cutting edge of the field.

Today, the platform can handle one table of standard rows and columns for processing. Gordhandas said that the company intends to expand in the future to “image processing, audio processing, video processing, unstructured data processing” so that the platform can be applied to as diverse a set of data sources as possible

Gordhandas says that Intersect is attempting to sit in the middle of more specialized machine learning platforms that are limited to hyper-focused niches, while also offering more analytical power than comparably simpler solutions.

Certainly the space has seen a proliferation of options. New York City-based Generable (formerly Stan) uses Bayesian modeling and probabilistic programming to improve drug discovery, while Mintigo uses AI modeling to improve customer engagement. A huge number of other startups target different stages of the data analysis pipeline as well.

In the end, Intersect hopes to make these tools more widely accessible. The company has a couple of early customers already, and is going through the Y Combinator  accelerator this batch.

The post Machine learning for everyone startup Intersect Labs launches platform for data analysis appeared first on Artificial Intelligence.

With more access to data and growing computing power, artificial intelligence (AI) is becoming increasingly powerful. But for it to be effective and meaningful, we must embrace people-first artificial intelligence strategies, according to Soumitra Dutta, professor of operations, technology, and information management at the Cornell SC Johnson College of Business. “There has to be a human agency-first kind of principle that lets people feel empowered about how to make decisions and how to use AI systems to support their decision-making,” notes Dutta. Knowledge@Wharton interviewed him at a recent conference on artificial intelligence and machine learning in the financial industry, organized in New York City by the SWIFT Institute in collaboration with Cornell’s SC Johnson College of Business.

In this conversation, Dutta discusses some myths around AI, what it means to have a people-first artificial intelligence strategy, why it is important, and how we can overcome the challenges in realizing this vision.

An edited transcript of the conversation follows.

Knowledge@Wharton: What are some of the biggest myths about AI, especially as they relate to financial services?

Soumitra Dutta: AI, as we all know, is not new per se. It has been there for as long as modern computing has been around, and it has gone through ups and downs. What we are seeing right now is an increased sense of excitement or hype. Some people would argue it’s over-hyped. I think the key issue is distinguishing between hope and fear. Today, what you read about AI is largely focused around fear — fear of job losses, fear of what it means in terms of privacy, fear of what it means for the way humans exist in society. The challenge for us is to navigate the fear space and move into the hope space. By “hope,” I mean that AI, like any other technology, has negative side effects – but it also presents enormous positive benefits. Our collective challenge is to be able to move into the positive space and look at how AI can help empower people, help them become better individuals, better human beings, and how that can lead to a better society.

Knowledge@Wharton: How do you get to the “hope” space in a way that is based on reality and away from the myths and hype?

Dutta: We need to have what I term as a “people-first” AI strategy. We have to use technology, not because technology exists, but because it helps us to become better individuals. When organizations deploy AI inside their work processes or systems, we have to explicitly focus on putting people first.

This could mean a number of things. There will be some instances of jobs getting automated, so we have to make sure that we provide adequate support for re-skilling, for helping people transition across jobs, and making sure they don’t lose their livelihoods. That’s a very important basic condition. But more importantly, AI provides tools for predicting outcomes of various kinds, but the actual implementation is a combination of the outcome prediction plus judgment about the outcome prediction. The judgment component should largely be a human decision. We have to design processes and organizations such that this combination of people and AI lets people be in charge as much as possible.

There has to be a human agency-first kind of principle that lets people feel empowered about how to make decisions, how to use AI systems to make better decisions. They must not feel that their abilities are being questioned or undercut. It’s the combination of putting people and technology together effectively that will lead to good AI use in organizations.

“The key issue is distinguishing between hope and fear…. The big challenge for us is to navigate the fear space and move into the hope space.”

Knowledge@Wharton: That’s an ambitious vision — of being people-centric in the way you think about AI. What are some of the challenges involved in realizing this vision?

Dutta: Some are technological challenges, and some are organizational challenges. In terms of technology, AI systems are broadly defined in two categories. First, there are the traditional ruled-based systems, systems that are based on if/then kinds of rules. These are much easier to integrate, partially because they can be explained logically, in human, understandable terms.

The second category, which is much more exciting — and which has had some of the most impressive results — involves the application of deep learning, neural networks, and other kinds of related technologies. These technologies, unfortunately, are still largely black boxes. It’s hard to explain the complex mathematics inside these boxes and why they come up with some outcomes and not others. Given the lack of transparency, it sometimes becomes hard for human beings to accept the outcome of the machines. Introducing more transparency into the prediction outcomes of AI systems is the technological challenge that many scientists around the world are trying to address.

The organizational challenges are equally complex, if not bigger. How do you design work systems and work processes that leverage the best of people and machines? This requires the ability to not just blindly follow the path of automation because it seems the most cost-effective way to handle things, but to also have the patience to understand how to redesign jobs. Jobs have to change as you implement AI systems inside organizations. That requires the ability to support people as they make transitions and to invest in their development and re-skilling.

AI systems, like many technological systems, provide additional support to people. This support has to make people feel more empowered. If systems don’t make people feel better about what they do, they will fail in terms of getting acceptance in the organization. So there are a lot of human issues and managerial issues involved in making sure that companies present a people-centric or a people-first approach to AI.

Knowledge@Wharton: If we look at the broad range of financial services and banking, in which sectors do you see the most disruption — or maybe the most innovation — through AI?

Dutta: The whole financial sector is ripe for the application of AI, because finance is extremely data-intensive. It has been on the forefront of technology applications. I would argue that AI could transform every single decision-making process in the finance sector because you have volumes and volumes of data. Traditionally, financial organizations primarily operated with only financial data. But now they are able to integrate social behavior as well as social media data. They can combine the human social side, the behavioral side, with the financial side. The complexity of data has increased tremendously, so how do you handle that kind of data complexity? AI has the best set of tools to handle this.

What I see at present is that financial organizations are experimenting. We’re trying to understand how to apply AI creatively and productively. One should not forget that this phase of applying AI in organizations is relatively new. Even in the case of leaders like Amazon and Google, it was only about seven or eight years ago that these organizations decided to focus in an important way on AI. There was a process of experimentation and building strength in R&D and also exploring what can make sense. That process of building big strength, of trying to explore ideas with AI, is only now starting in the financial sector. So we have not yet seen the full impact of AI in finance. We’re just starting out on this long path.

“It’s the combination of putting people and technology together effectively that will lead to good AI use in organizations.”

Knowledge@Wharton: There’s an interesting debate going on between how the U.S. or Western financial institutions are using AI relative to Chinese companies. In this competition between the U.S. and China, who do you think will take the lead in innovation and AI?

Dutta: It’s important to first understand what makes AI systems powerful. The general consensus is that AI technologies per se haven’t seen any massive breakthroughs. What has happened is much more data is available now for training AI systems. We also have much more computational power for running through different algorithms. There is also a lot more effort being spent on engineering.

Typically, when you build an AI system, it’s not a clean application where you write the algorithm and it works. Instead, you have to have 20 different models, try out 50 different data sets and look at different heuristics about what works. There is a combination of many approaches and a lot of testing that goes into obtaining an effective end outcome.

If you look at the elements that make for a successful AI system, what you see is that on data, China has a natural advantage because of its large population and the number of people doing online transactions. Chinese companies, at least the digital leaders, are sitting on enormous volumes of data that dwarf some of their American peers. This gives them an advantage. When it comes to computational resources, the U.S. has an edge in the design of advanced microprocessors and custom AI chips, though China has amassed a world-leading concentration of computing power. Engineering requires a lot of manpower to experiment, to build out systems and to try different variations. China has cheaper labor and also more manpower in terms of sheer numbers. So when we look at the three components, it’s quite likely that China is going to have an edge.

Data privacy is another big issue. In the U.S., there is some clarity on who owns the data and how it can or cannot be used. In China, that’s unclear as of now. Is it the company that owns the data? What kind of access does the government have to it? Who can use the data? So Chinese AI companies might have some challenges when it comes to their international growth.

Knowledge@Wharton: Is use of data an area where international regulations can play a role?

“Given the lack of transparency, it sometimes becomes hard for human beings to accept the outcome of the machines.”

Dutta: Yes and no. Things are moving so fast that regulators, in general, are behind. The European Union is probably the best-known example of a region trying to regulate data users. They’ve done some good work with the GDPR (General Data Protection Regulation). Some states like California are putting data privacy regulations in place. But I think it’s important to have a coordinated approach.

The ultimate goal should be that the customer owns the data and the customer should be able to decide who uses the data and under what conditions. But we are far away from that. The reality is that the large companies in the world — it doesn’t matter which part of the world they come from — have enormous power. Most people don’t read the fine print when they sign user agreements. The balance is tilted in favor of large private players. Regulation is behind, and customers don’t have the tools to manage the data themselves. We have a turbulent period ahead of us.

The big players — who have enormous data stores — will be reluctant to give it up because a lot of their competitive advantage is based on that. Unless there’s strong regulation or a vigorous consumer backlash, it’s not going to happen. I don’t see a strong consumer backlash happening because consumers are seduced by free applications and the convenience factor. Who’s going to give up Google search? Who’s going to give up other free services? People are increasingly accepting the fact that they’re losing control over their data in return for free services. So, regulation is probably the best-case scenario, but again, regulators have been relatively behind in most countries.

Knowledge@Wharton: One major question that keeps coming up about AI is what will happen to jobs — especially at the lower levels in organizations. How can the people question be dealt with in this regard?

Dutta: This is probably one of the biggest questions facing policy and society. What will be the impact of technology on jobs? If you look at the last 100 years, with the exception of the Great Depression in the U.S., the U.S. growth rate has been relatively constant, and unemployment has been relatively within a fixed range. Many people argue that technology has come and gone, but the U.S. industry has somehow adjusted. Yes, there have been shifts, people have lost jobs but they have also gained jobs. On the whole, employment has grown.

The big question in front of us today, to which we don’t have a clear answer, is what will happen with AI now? AI is different in the sense that it does not just look at automating — or potentially automating – lower-end jobs but also higher-end jobs. In medical domains, for example, many AI systems perform at a higher level than the best doctors.

Clearly, machines will do some jobs entirely or very substantially. We have to decide how to handle the people who are displaced. It is an issue of organizational leadership and policies, and of national initiatives and regulation. It is an issue of how you support the growth of new industries. If AI is allowing the growth of new industries, is the economy flexible and entrepreneurial enough to support that growth?

“If you have two partners who need to coexist, and one has some limits while the other does not have any limits, then how do you handle that merger?”

There are a lot of micro and macro issues in terms of supporting change, allowing new sectors to flourish, enabling people to learn new skills, and so on. That’s what makes the whole thing so complicated. It’s a challenge that we have to face collectively, because if you don’t get it right, there will be massive dislocations in society. The issues are solvable, but they have to be solved with collective action and determined leadership.

Knowledge@Wharton: If you gaze into your crystal ball, what do you see coming down the road?

Dutta: The next five to 10 years are going to be very important in determining how AI is used in society. The impact of AI will play out over several decades. That’s one reason why universities like Stanford are publicly committed to studying the impact of AI for the next 100 years. We have to start understanding what the possible implications are. In many cases, we don’t know what we don’t know about AI. What will the impact be? How will people react when systems become more intelligent, when they are capable of more autonomous learning? How will their behavior change? And especially if these machines are black boxes, how will we understand the interactions of the machines with human beings in society?

I don’t want to make it sound like science fiction, but it’s an important question ahead of us. Ultimately, we have to have people and machines coexisting in an effective manner. The challenge is that the machines’ capability is increasing — in some areas exceeding human beings — and potentially with no upper limits. That, I think, is the interesting challenge. If you have two partners who need to coexist, and one has some limits while the other does not, then how do you handle that merger?

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