It’s difficult to imagine that behemoth companies like Facebook and Google were once humble startups. Jeff Bezos started Amazon in his garage at the age of 30. Today, he’s the world’s richest man, with Amazon accounting for nearly 50% of all online sales in the US in 2018, and reporting a market capitalisation of $755.7 billion at the beginning of 2019.

We’re often told that companies are here to change the way we live for the better. Facebook has sold itself as being the world’s social network, connecting billions of people around the globe, and has even set its sights on launching its own digital currency, Libra, which could theoretically bring financial infrastructure to those in the developing world who have no access to banks.

Whether or not these companies are actually changing the world for the better is debatable. Monopolistic tendencies, partnered with numerous privacy concerns have muddied their image. Along with a growing focus placed on rampant bullying and mental health issues arising from social media use, these problems are taking the sheen off of the whole notion of global connectivity and community. It is undeniable that big tech has the resources, knowledge and influence to do a lot of good in the world, but many people just aren’t seeing it.

Should we instead be looking towards startups to offer the kind of philanthropic initiatives that could spawn real social change? Small grassroots companies are finding market gaps and interesting investors who see the potential for startups to make a real impact while still generating revenue. We’ve taken a look at how this is possible.

Social Entrepreneurship

Changing the world costs money, and that’s a cold, hard capitalistic fact. There is no shortage of good intentions, but without the finances to hire staff, build a product or service and market it, the only thing that will change in the world is that another company will have contributed to the statistic that 90% of startups fail.

VC investment has historically been drawn to the future unicorns waiting in the wings, but that may be a simplistic generalisation going forward. Today, there is a new subsection of startup funding gaining traction. Over 1000 startups are now listed on Crunchbase under the ‘Social Entrepreneurship’ umbrella, with 617 of these categorised as for-profit companies. In total, these 1021 companies have raised more than $1.6 billion in funding.

Big Business Leading the Way

The above stats are encouraging, but interestingly enough, there is not necessarily a widening gulf between the philanthropic ideals of small startups, and the profit-hungry rigidity of larger companies.

In 2018, Larry Fink, CEO of one of the world’s largest asset management firms, Blackrock, sent shockwaves around the investment industry by proclaiming, “To prosper over time, every company must not only deliver financial performance, but also show how it makes a positive contribution to society.” Making such a public push for social responsibility as the head of an economic powerhouse that holds more than $6 trillion in assets put Fink in a position to encourage change with CEOs on Wall Street and beyond.

How big an effect this will have is a matter of contention. Figures quoted before Fink’s Blackrock open letter noted that out of the then $193 trillion in the global financial markets, only $7 trillion qualified as socially responsible investing (SRI), while impact investing represented $114 billion. A key distinction between the two is that SRI focuses on minimising socially negative impacts of investments in general, while impact investment directly aims to finance projects that have positive social impact as their primary goal.

Salesforce is another company that has pledged business practices geared towards social change. In 2017, it announced a $50 million initiative to fuel social impact startups. This Impact Fund specifically targets four areas of investment to promote startup incubation: workforce development and education, equality, environment, and the social sector.

Incentives for Socially-Minded Startups

There is a growing ecosystem of programs that offer startups direct investment and mentoring resources. Ship2B has offices in Barcelona and Madrid, and directly targets and promotes technological projects with high social impacts. It also offers access to its existing ecosystem of companies, thereby promoting a collaborative environment for startups to promote social change.

Another resource is BBVA Momentum, which offers a five-month program consisting of training, strategic support and potential access to funding opportunities, all designed to promote growth and networking opportunities for socially-minded entrepreneurs.

The access to such schemes varies between countries around the world, as Ship2B’s co-founder, Xavier Pont, noted when he pointed out, “The U.K. is 15 years ahead of us. They started with accelerators, incubators, funds and consulting firms. At a certain point, these investors manage to get the government to make a firm commitment to impact investing and social entrepreneurship.”

The Startups Saving the World

Perhaps the most widely known socially-minded startup, Change.org has amassed over 150 million users in 196 countries by providing an easily accessible platform to create philanthropic and educational campaigns that raise awareness and generate support for social issues.

Recognising the power of technology to positively impact the planet has led to Code.org becoming a key driving force in making coding education more accessible for all. This Seattle-based startup works to expand computer science programs in schools, and to increase women’s and minorities’ participation in this field.

The ongoing problem of rampant food wastage is being directly addressed by Karma’s app-based marketplace that allows users to buy unsold food at discounted rates. This is helping restaurants and shops to reduce their waste levels, while giving lower-income families access to reasonably-priced groceries and meals.

A startup producing an actual physical product, Stockholm-based Altered has developed a nozzle that can be fitted to standard taps, reducing water usage by up to 98%. For its cost-effective and eco-friendly manufacturing, this climate change-targeting company was declared winner of the 2018 Climate Solver Award.

Can Startups Save the World? was originally published in Primalbase on Medium, where people are continuing the conversation by highlighting and responding to this story.

The process of startups taking software from concept to rollout is far from straightforward. Coming up with a dynamite idea for a product is one thing. Agreeing on how to build, maintain and scale it is quite another, and can often devolve into a development team at loggerheads with each other and the CTO.

Constructing the architecture of an application traditionally draws people into one of two camps, which is often where these disagreements stem from. For some, the more traditional concept of a unified, centralised software framework is the ideal situation, such as those utilised by tech companies like Uber in their early stages. This is approach is known as ‘monolithic’.

However, in recent years, a new modular approach has become increasingly prevalent in startups’ product development. Dubbed ‘microservices’, this new approach to application architecture is far more decentralised. It offers developers the ability to work on individual parts of a larger software framework separately, yet still have it as part of a larger unified structure.

Below, we’ll take a closer look at microservices, and weigh up their pros and cons against the traditional monolithic structure for software development teams.

What are Microservices?

An application being developed by a tech startup may appear to be simple, but even the most outwardly simple products require a great number of complementing systems and data sources working in tandem.

It is this modular nature of software products that have given rise to microservices. The basic idea is to allow developers to divide an overall application into a collection of smaller, independent services, each of which has its own data store and communicate with one another through an API. The ‘plug and play’ nature of microservices allows them to be developed, altered and replaced with ease, with no impact on the functioning of the overall application they are part of.

These microservices, while making up a larger unified system, do not have to share the same coding architecture as one another. Developers can build one service using MongoDB and Java, while using Ruby for another.

The Rise of Microservices for Startups

The centralised monolith architecture was the undisputed norm for years. We even saw the biggest tech startups such as Netflix and Uber initially building their applications this way. However, as their businesses have scaled and diversified, both have branched out into microservice development with Uber, for example, sharing how they achieved this in a blog post on their service-oriented architecture.

Why have microservices been adopted by so many startups in recent years? A large contributing factor is the reduction in technical limitations, aided by the proliferation of cloud services such as AWS. Now, the lower price points and ease of access to cloud services aids the development of distributed applications, especially for startups, who can adjust their resource load geographically. In addition, startups can now take advantage of lower costs for RAM, multicore machines, and faster, more reliable network connections.

Scope for Blockchain Integration

For microservices to be utilised to their full potential, the handling of developing and integrating smaller applications into the larger architecture must be agile, with a key focus on the fast distribution of work.

Developers are sharing solutions using open source code, but the logistics of having a huge collection of microservices communicating across a crowded API landscape is posing security questions, especially considering the large number of participants in the larger microservices ecosystem.

Blockchain is presenting itself as a possible solution for the API security issue. Microservices rely on digital coordination as part of a distributed ecosystem, especially in relation to their own respective coding architectures and data sources. Therefore, the integration of blockchain as the key coordination mechanism could allow enterprises to realise the benefits of trustless and agile data coordination, also adding in a level of control over the ways microservices interact with their data.

With all this taken into account, we’ve drawn up a list of pros and cons that startups should consider before committing to building their application architecture around the microservices model.

Pros

Independent Deployment

Microservices are a boon for developers looking to roll out updates and upgrades to application architectures, thanks to their modularity. Microservices can be swapped out individually, without the need to take the entire application offline, as can be necessary when using the more traditional centralised monolithic structure.

Developer Independence and Specialisation

Smaller dev teams can be formed to focus on specific microservices, which can speed up internal processes, and allow for easier hiring of specialised skill sets. Faster development cycles can also become possible.

Scalability and Performance

Microservices allow for easy scalability because of their independence from one another. As operational demands increase on one of these services, it is easier to detect these hot services and bottlenecks within the larger framework of the application and respond accordingly.

Avoiding Technological Lock-in

Developers have the option of building microservices using different languages and technology stacks, which avoids the over-reliance on and long-term commitment to any particular vendors.

Cons

Complexity

Each microservice is its own application, which requires its own testing, release and ongoing monitoring. For delivery workflow to be efficient, it often has to be automated, which requires additional work and infrastructure to accommodate.

Business Organisational Changes

For each microservice dev team to have the independence required to adequately manage their own products, there needs to be a clear and defined shaping of the overreaching business structure to allow for proper delegation of responsibilities for technical decisions and new builds. This can cause friction in more traditional management structures.

Unreliable Communications

As microservices run on their own separate processes and communicate over APIs, there is the risk of communication failure between different points of the application. Multiple databases need to be updated, so developers are faced with challenges to ensure that each of these points is functioning correctly.

Operational Overheads and Performance

Because microservices are frequently deployed on their own virtual machines, this can require more dedicated VM work for developers. Also, communication over a network is invariably slower than in memory. Companies need to ensure that their network speeds are maintained over the course of a product’s development.

Microservices offer a lot of benefits for startups if they choose to properly invest in the organisational adaptations that they require over the more traditional monolith infrastructure. As stated previously, hugely successful startups such as Uber have seen great success in adapting their monolithic structures into microservices, but their success should not be taken as a ‘one size fits all’ rule. Instead, it is essential for startups to consider their intended company growth projections, and whether they can seriously foresee an infrastructure change as viable.

Microservices vs Monoliths: A Guide for Startups was originally published in Primalbase on Medium, where people are continuing the conversation by highlighting and responding to this story.

As society becomes increasingly data driven, the importance of ensuring this data’s security and fidelity grows exponentially. While prevention is still the best medicine though, tracking down those who have already entered your system is also vital. This has led to the growth of digital forensics — a field of cybersecurity focused on tracking down those who have hijacked the internet for their own nefarious ends.

Television shows such as CSI and NCIS have long presented a stylised version of digital forensics teams, doing battle with mysterious hackers who bombard their screens with flashing red skull-and-crossbones graphics and other such clichés. However, the reality is far more subtle, further reaching, and more important.

To find out the realities of life in the field, we spoke to Brett Shavers, a former police officer and digital forensics investigator assigned to various state and federal cases. Brett has written several award-winning books on the Digital Forensics Incident Response (DFIR), and is an adjunct professor in digital forensics at the University of Washington.

An Evolving Field

Shavers founded his police department’s digital-forensics division in a small storage closet. Today, he commands large crowds at conferences, companies, and universities. He has been on the frontline during the rapid evolution of computer security and analysis, and witnessed as the talent to deal with it evolved too.

“The granularity of information we can pull from data today is incredible compared to years ago, when I first started in the field,” says Shavers. “We’ve learnt to dig deep in logs and databases to determine incredible details of computer-user activity that we never before knew existed on storage media. Today’s tools are amazing at being able to recover this data and give output that is easy to interpret. Coupled with the fact that universities now award advanced degrees in digital forensics, we have both the technology and educated practitioners we didn’t have a decade ago.”

“We’ve learnt to dig deep in logs and databases to determine incredible details of computer-user activity that we never before realised existed on storage media”

While the field of cybersecurity has evolved, however, the scale of data today is such that individuals actually feel powerless to protect themselves. Public knowledge needs to keep pace with technological progress or it will be for nothing.

“The average person is overwhelmed by how much data is stored and can be recovered by not only the government, but by criminal actors,” says Shavers. “Indeed, the amount of information available overwhelms them to the point that they may feel there is nothing they can do to protect their data, especially as it’s a constant effort to maintain control of personal information. Until we realise that personal protection requires constant effort, few will make the effort last longer than an initial or sporadic short-run effort.”

Until this happens, there will inevitably be cyber attacks. And this means digital forensics.

Industry Need For Digital Forensics

Lack of understanding as to what constitutes a sustainable plan to safeguard data is inevitably carried over into the workplace. Smartphones facilitate personal and professional email accounts syncing on to one device, with apps such as Google Drive also contributing to the blurring of definitive work/social device use. Granted, many workers are still issued with company smartphones, but this does not necessarily mean their usage of these phones differs when it comes to adequately protecting their data.

“I typically see practically nothing proactive being done — or if it is done, companies doing it completely wrong — or I see incredible work in setting up defences against malicious actors”

DFIR can become a necessary expenditure for a company when there are unclear or non-existent policies in place to govern employee data practices. This lack of safeguarding can lead to malicious actors having a much greater chance of compromising internal systems. Despite the relative flexibility of a company’s options to create digital-defence strategies, Shavers notes that the reality is far more black and white:

“I haven’t seen much of a middle ground. I typically see practically nothing proactive being done — or if it is done, companies doing it completely wrong — or I see incredible work in setting up defences against malicious actors in incident response.

“From what I have seen, it really depends on the individual company as to how much effort and expense they choose to go to up front to prevent theft and damage to their systems. I don’t believe that being a victim in the past makes as much difference as the leadership choosing to be proactive.”

Digital Forensics vs Manufacturer Encryption

The good practice of not opening randomly-received .exe files, or avoiding sending sensitive details to unsolicited emails have long been hammered home to consumers. With Google, Facebook, the NSA and many others now harvesting tremendous amounts of user data, the idea of private user data is increasingly laughable. It’s no longer a case of avoiding hackers’ attempts to steal information from a home computer, but, rather, understanding the breadth of information that can be collected with implied consent.

Following the 2015 San Bernardino terrorist attack in the US, the FBI and Apple became locked in a series of disputes over whether manufacturers can be compelled to unlock mobile phones for law enforcement agencies. Historically, iPhones that have cryptographically protected data cannot be forcibly unlocked, even by Apple themselves. The FBI requested that Apple write dedicated software that would allow agents to circumvent the encryption and four-digit passcode, allowing complete access to the device of one of the attack perpetrators. Apple declined, which resulted in the scheduling of a United States District Court for the Central District of California case against Apple by the FBI.

This legal situation, in particular, highlighted the degree of encryption that iPhone users perhaps didn’t expect, and the lengths to which certain manufacturers would go to protect their privacy.

“Ideally, private companies protect the data of their users because the users are paying for the services,” says Shavers. “Practically speaking, any government — local, state, and federal — can compel any private company to provide any data that a court demands through subpoenas and search warrants. The only options for private companies to not cooperate are to argue in court against government demands, or stop providing services that collect user data.”

“Practically speaking, any government — local, state, and federal — can compel any private company to provide any data that a court demands through subpoenas and search warrants”

The above case highlights a complex matter for digital forensics. As the adoption of consumer devices such as smartphones continues to increase, the sophistication and number of applications of which they’re capable will also grow. Edward Snowden alleged in 2013 that various surveillance agencies, including the UK’s GCHQ, could access almost all user data in iOS, Android and Blackberry phones, which likely contributed to Apple’s increased security and encryption standards in iOS 9. Digital forensics are now faced with levels of encryption that manufacturers don’t even have the root access to break.

Legislation and Digital Forensics

Lawmakers are at odds as to how to deal with this reality. Following the aforementioned FBI-Apple case, US Senator Dianne Feinstein has twice tried to spearhead new legislation that requires manufacturers such as Apple to allow access to all users’ encrypted data, should a legal challenge arise. Her initial attempt in 2017 didn’t make it to the floor of Congress, but she initiated a new attempt in 2018.

“Legislation will always be behind current technology,” Shavers says, “much like anyone working in the field is behind, simply because major advances occur faster than any law or person can effectively respond. Knowing this is the norm shouldn’t prevent attempts to keep up, but should encourage constant improvements in response to incidents, and constant updates to laws relevant to changes in technology.”

As the FBI-Apple case highlights, updating laws as technology progresses isn’t always an easily achievable reality. Not only has encryption technology progressed, but the encryption complexity of products by the likes of Apple has resulted in consumers enjoying increased protection standards that new legislation will seek to remove.

“One of the fields that needs improvement is the updating of outdated laws or, in some cases, their complete removal — some not only don’t apply any more but may criminalise acts that, by virtue of changing technology, are no longer criminal. Ethical hacking, for instance,” says Shavers.

“Some [laws] may criminalise acts that, by virtue of changing technology, are no longer criminal. Ethical hacking, for instance”

The skill set of digital forensics lends itself well to hacking, which many large companies have recognised and sought to utilise. Ethical hacking has allowed companies such as Google and Facebook to reward individuals who have discovered exploits in major consumer platforms and products through the so-called ‘bug bounty’ programs.

The Future of Digital Forensics

With data production increasing, there is no question about the ongoing need to decrypt and analyse devices and online storage resources. However, what remains to be seen is the ability of digital-forensics experts to effectively tackle the vast data streams.

“With electronic data propagating like bunnies, I see digital forensics focusing more on relevant data captures over the more traditional complete data captures,” says Shavers. “For example, rather than creating full images of terabytes of data to later sift for evidence, triage and disperse, it may become more common to selectively target known areas of compromise or locations where electronic evidence is typically stored.”

Another area that holds the potential to benefit the digital-forensics field is the adoption of blockchain on a wider personal and industrial scale. In any kind of investigation, a forensics expert is concerned with the ease of access to the data in question, but also with ascertaining the data’s authenticity and fidelity. Because of blockchain’s foundation of providing an immutable ledger of all transactions, it could theoretically act as a whole new medium via which digital-forensics professionals can streamline their data analysis.

“I can’t wait to see how blockchain technology will be practically applied to digital forensics and incident response,” says Shavers. “Up to this point, most of what we see is the marketing tactic of simply using blockchain as a buzzword. I do see some application of the blockchain in the field of DFIR, but I’m keen to see how companies will actually make a use study out of it.”

“I can’t wait to see how blockchain technology will be practically applied to digital forensics and incident response. Up to this point, most of what we see is the marketing tactic of simply using blockchain as a buzzword”

Blockchain’s infancy as a platform, combined with a saturation of speculation concerning its potential applications, does make it difficult to effectively predict just how useful it may prove to digital-forensics experts seeking new methods of collecting high-quality, uncompromised data. With experts such as Brett Shavers taking an interest in its development, however, it’s likely that companies that are proceeding with their own blockchain development will have factored in its digital-forensics implications.

John Murray is a senior editor at Binary District Journal, where this article was originally published.

Virtual CSI: How Real Life Digital Forensics Investigators Track Down Hackers was originally published in Primalbase on Medium, where people are continuing the conversation by highlighting and responding to this story.

Bitcoin has surged past the $11,000 mark, successfully navigating the choppy waters thrown up by Donald Trump’s recent stinging tweet attack on Facebook’s proposed Libra coin and all other cryptocurrencies. The President doesn’t like these digital assets, saying that they are ‘not money’, that they facilitate illegal activity, and that they need to be regulated. If such damning words from the Commander in Chief were designed to halt Bitcoin in its tracks, they haven’t worked in the short term.

No matter what your opinions are on Trump, his words (and outbursts) have enormous power to sway popular opinion and market direction. Bitcoin’s resilience to these recent headlines is acting as validation to many who are looking to the future for the currency, including its potential uses in the more traditional financial structure of salary payments to employees.

When dealing with a person’s wages, Bitcoin justifiably comes under additional scrutiny to add to its existing tally. However, the increasing exposure that virtual currencies are receiving, coupled with Bitcoin’s recent market resurgence, is making this an idea worth considering for many people.

We’ve taken a look at the reality of being paid a salary in bitcoin — the pros and cons, as well as those who are leading the way in expanding cryptocurrencies’ use in mainstream economics.

The Current Climate

The option to accept a salary in Bitcoin only became available a few years ago, and was mostly confined to crypto startups offering it to their employees. In 2013, Coinbase only had six employees, and paid all of them in Bitcoin.

More recently, a mixture of high profile individuals and major corporations around the world have made headlines by adopting cryptocurrency payments, thereby bolstering the legitimacy of the practice in the eyes of many. Canadian speed skater Ted-Jan Bloemen accepted his payment at the 2018 Pyeongchang Winter Olympics in Bitcoin, in a sponsorship deal with ONG Social, and virtual reality provider CEEK VR.

Japanese Internet firm GMO Group also announced in December 2017 that they were offering 4,000 employees the option to receive a portion of their salaries in bitcoin. The company had recently expanded into cryptocurrency mining and trading, and commented that the move was important for “nurturing and developing cryptocurrency literacy”.

Dedicated companies have now been set up to cater for the growing interest in partial salary payments via cryptocurrencies. BitWage is a payroll company that allows individuals and companies to send and receive salary portions in Bitcoin and other currencies, with advertised users from major industry names including Airbnb, Uber, Facebook and American Express.

Legal Hurdles

Of course, the above examples are far from representative of a universal reality for Bitcoin. Cryptocurrencies may be gaining traction and achieving recognition, but international financial structures and the regulatory bodies governing them are still scrambling to catch up.

In many cases, the problem is deeply rooted. Depending on the country you’re in, Bitcoin may be illegal to varying degrees. Bitcoin has never been legal in any capacity in Bolivia, for example, while in Ecuador the currency was outlawed in mid-2014 as part of the country’s financial reforms. Many have seen the move as a way of removing competition from the country’s own digital currency system, Sistema de Dinero Electrónico. However, Bitcoin laws aren’t strictly enforced, making it a possibility for some Ecuadorians to use it on a limited scale.

China’s relationship with Bitcoin is more complex, with numerous headlines over the years stating that the country has banned crypto exchanges and made mining illegal. The Chinese government has recognised and protected bitcoin as virtual property since 2013, but does not recognise it (or any other crypto-assets) as currency. While ICOs have been banned in the past in China off the back of crypto scams, in reality, citizens are big traders of Bitcoin and other currencies via locally managed WeChat groups.

For those living in countries where Bitcoin restrictions are less prevalent, there are still key considerations that make a straight switch from fiat to crypto difficult. Like China, the IRS in the US considers Bitcoin property rather than currency, while the Fair Labor Standards Act requires that employers pay their employees “cash or negotiable instruments payable at par.”

The fact that legislation still doesn’t know what to make of cryptocurrencies in many respects makes the prospect of receiving a Bitcoin salary a complex prospect. Let’s take a look at a simplified breakdown of the pros and cons.

Pros

Fast and Borderless

The nature of industry and commerce is truly international today, with an ever-increasing number of employees operating remotely. Bitcoin payments can easily be sent anywhere, with the luxury of not having to deal with international banking, including expensive wire fees, conversion rates, delays and holding periods. Bitcoin transactions do include fees, but these are far easier to manage than those imposed by traditional financial institutions.

Easy Investment

Bitcoin and other digital assets can be seen as an easy way for employees to get involved in the complex world of investments. Rather than navigating complex stock options and investment packages offered by brokers and banks, direct payment in Bitcoin allows an individual to take direct and instantaneous control over their own simplified cryptocurrency portfolio.

Startup Culture

Employers, particularly in the tech startup sphere, are seeing Bitcoin payments as a way of attracting new talent and complementing this with other key perks. Keeping an open mind in terms of accepting crypto in lieu of fiat currency may open doors to some lucrative employment opportunities within certain industries.

Cons

Volatility

It is undeniable that Bitcoin, along with other cryptocurrencies, can be incredibly volatile. The market is famously unpredictable, and anyone accepting Bitcoin for their salary could see the value plummet, as well as skyrocket. There needs to be careful consideration by an individual over what they can afford to lose.

Tax Implications

As highlighted above, this model of salary payment opens up tax questions which, depending on your location, can become quite complex. In the UK, HM Treasury issued guidelines in 2018 which stated that cryptocurrencies received as employment payments are subject to national insurance and income tax, but there are further underlying considerations in other jurisdictions, such as capital gains that must also be factored in.

Few Participating Companies

The notion of Bitcoin salaries may be an appealing one for many individuals, but their employers often don’t share that sentiment. Cryptocurrencies still carry a great deal of stigma in many circles, and the perceived risks and legal implications that come with moving payroll over to this new financial concept.

Taking the plunge and switching to Bitcoin for salary payments is a big step and, in the end, there is no definitive answer that can be applied for every individual. Realistically, the turmoil that surrounds Bitcoin as a form of currency is likely to continue for the foreseeable future, especially with Donald Trump now fixing it in his sights. Interestingly though, his former White House chief strategist Steve Bannon recently countered the president, saying that “cryptocurrencies have a big future.” How this back and forth may progress between the two is anyone’s guess.

Should I Get Paid in Bitcoin? was originally published in Primalbase on Medium, where people are continuing the conversation by highlighting and responding to this story.

Buy low. Sell high. That’s the underlying goal for any kind of investment, but navigating the financial markets is far more complex than this and involves a great deal of volatility. The big firms dealing with hedge funds and multi-billion dollar portfolios are always looking for new ways to stay ahead of the curve when it comes to analysing market trends and developing strategies that will see reliable returns for their clients.

This is exactly why machine learning has attracted so much attention and investment in the financial trading world. Firms including JPMorgan and Morgan Stanley have become embroiled in a high-tech arms race in recent years, pouring billions of dollars into incorporating machine learning platforms into their infrastructures and hiring developers and researchers into dedicated data science equity teams.

How widespread is the use of machine learning within the hedge fund investment market, and how much of an impact has it already made?

Investing is a Data Goldmine

Machine learning is built upon a solid bedrock of high quality, high volume data. The financial industry already utilises it across the board, and has seen the evolution of different types of funds and specialised traders emerge to exploit it. A quant fund is a type of investment fund that selects specific securities through quantitative analysis, by means of specialised traders, often referred to as ‘quants’ themselves, building complex software models. High-frequency trading applications are typically coded in C++, while offline models can utilise MATLAB and SAS.

Quant funds have already established themselves as a significant avenue of investment strategy within financial markets, with some estimates in 2017 reporting quant fund managers as being responsible for 27% of all US stock trades.

Big Banks are Going Further

Machine learning and AI development has been the next logical step for massive financial institutions looking for more effective and future-proofed ways of exploiting their data streams. The goal of these technologies is to allow hedge fund managers to find trading signals based on historical data, with the minimum level of human intervention possible.

The concept is an appealing one and hedge fund managers have been quick to adopt the technology to aid their own trading processes and portfolio diversification. A 2018 BarclayHedge survey found that more than two-thirds of respondents said that they used AI/ML in some capacity to inform investment decisions and optimise their portfolios, while more than a quarter have used the technologies to automate their trading.

JPMorgan and Morgan Stanley have been the industry leaders in the aforementioned AI arms race, but several other institutions are also pouring investment into the arena. Bridgewater Associates is one such hedge fund giant that is focusing heavily on its machine learning infrastructure, as well as Simplex Asset Management in Tokyo, representing notable interest from the Asian markets.

The most recent development in the field is JPMorgan’s strategy to invest in established and emerging machine-learning statistical-arbitrage hedge funds. This suggests not only a plan to secure their own machine learning infrastructure but also speculation that this emerging section of the financial market is ripe for exploration and early-stage investment.

A Shaky Start

Despite the relatively enthusiastic adoption of machine learning in hedge fund management, it has not been an entirely smooth roll-out. There have only been modest returns overall from the strategies developed by these machine learning methods, such as the 1.1% annualised return in three years from the Man AHL Dimension fund, compared with an almost 5% gain for the average hedge fund.

Machine learning integration into hedge fund trading, like its integration into any industry, takes time, money and highly specialised expertise. This expertise is in high demand across various industries, making the reliability of forming sufficient teams to push forward with suitable machine learning development hard to guarantee. For example, a quant unit of Man Group, Man AHL, needed three years of work to gain enough confidence in its machine learning technology in order to finally dedicate client money to it.

Always Expect the Unexpected

A large degree of trepidation remains with the pairing of machine learning and hedge fund investment. In order to successfully train machine learning models, there must be a degree of hand-holding by developers, with data streams carefully vetted and whittled down into their most tightly concentrated, relevant forms. However, financial markets can be severely affected by sudden, unpredictable events and revelations, so shouldn’t any machine learning model being integrated into hedge funds be fed the biggest amount of historical investment market data possible?

Unfortunately, doing so leaves the door open to these models finding patterns that are ultimately meaningless to hedge fund managers who require far more focused prediction parameters. And of course, algorithms still have trouble with bombshell events such as random terror attacks and political events such as Brexit.

Accountability is Key

Machine learning algorithms and artificial intelligence programs are often lambasted for a lack of transparency, thanks to the ‘black box’ nature of their development and functionality. In the world of financial trading, where billions of dollars must be accounted for, any monumental slip-ups and losses require a direct and easily traceable path of culpability.

The first-ever legal case concerning the liability of AI platforms in poor financial investments is being raised in London. Hong Kong real estate mogul Samathur Li Kin-kan is suing Raffaele Costa, CEO and founder of Tyndaris Investments, over the latter’s use of a supercomputer named K1 in a robot hedge fund. Li agreed to let the computer’s AI manage $2.5 billion of investments, but it began making heavy losses, including one particularly bad day where $20 million was wiped off the portfolio.

Such high profile cases involving AI liability were bound to happen sooner or later. It’s essential to look past the hype that often comes with machine learning integration into various industrial sectors, seeing instead its development as complementing existing skill sets rather than fully replacing them. Hedge fund managers are seeing machine learning tools not as a magic wand for great investments, but as a powerful tool which must be shaped in accordance with the confines of the markets.

The Money Makers are Embracing Machine Learning was originally published in Primalbase on Medium, where people are continuing the conversation by highlighting and responding to this story.

True intelligence is subjective. As machine learning progresses, we are witnessing AI programs capable of calculation, data recognition and broader analysis far beyond what humans are capable of. One aspect of intelligence that AI has been lacking, however, is true imagination. But is this for the best?

The potential for algorithmically-generated imagination has long been possible, but it has taken the implementation of a new direction of neural network interplay to be realised. Generative Adversarial Networks are a branch of algorithms that have recently garnered a great deal of attention, not only from the machine learning R&D community, but also from the mainstream media.

GANs utilise the power of two neural networks that are pitted in direct competition with one another. The goal is to create completely entirely new example data, particularly images.

How GANs Work

The generative network is initially fed training examples, e.g. pictures of dogs, and constructs completely new outputs that are sent to the discriminative network in order to be analysed and scrutinised for authenticity. If the discriminator receives a dog image from the generator and finds it to be inauthentic compared to the confines of the original training data set, it is rejected and returned to the generator, which then produces a new variation of the output. In the end, the generative network learns from its mistakes to a point where it can fool the discriminative network.

“GANs are the most interesting idea in the last 10 years in ML”

In essence, this is artificial intelligence training itself. Such is the groundbreaking nature of GANs, there is some debate as to what sub genre of machine learning they belong to. Because the ‘authentic’ images used to train the generative model are unlabelled, these adversarial neural networks are classified as unsupervised learning. Equally, the need for a form of initial training data to establish the parameters of the image has led some researchers and developers to dub it ‘semi-unsupervised’ learning.

Whatever their classification, GANs are currently responsible for machine learning’s main headlines, with numerous research papers, news stories and op-ed pieces attracting mainstream attention. In a 2016 online Q&A session, Yann LeCun, Chief Artificial Intelligence Scientist at Facebook AI Research, went so far as to argue that GANs are “the most interesting idea in the last 10 years in ML.”

Deepfakes: Society Killer?

Various media outlets have been quick to showcase the capabilities of GANs — in particular, the formation of so-called deepfakes. These are videos in which GANs have been used to insert celebrities’ faces and voices into pre-existing clips. Essentially, whatever the person in the original video is doing or saying, the celebrity has now replaced them. Steve Buscemi’s face and voice were inserted onto Jennifer Lawrence for her 2016 Golden Globes acceptance speech in one instance, while other notorious examples have included other famous actors inserted into porn clips.

To get a greater understanding of the potential scope of GAN applications, and whether or not they will be well received, BDJ spoke with two leading experts in the world of machine learning and artificial intelligence. Reza Zadeh is founder and CEO of Matroid, and an adjunct professor at Stanford, previously serving on the Technical Advisory Boards of Microsoft and Databricks. Rachel Thomas is the co-founder of fast.ai, and a professor at the University of San Francisco.

The Reality of GANs

Because of the relative infancy of the technology, the degree of autonomy that GANs are capable of is often misunderstood. Reza offers a tempering clarification concerning the current potential for GANs to craft the desired type of unique images, with no initial developer guidance.

“We’ve never been able to automatically generate realistic images until GANs came along. From a subjective PoV, I think that’s quite cool. However, with GANs, we have trouble prescribing what is generated, and need to give many examples of it, which seems counterproductive because: why are we generating pictures of something we already have? The reality is we can use the quirks of the generation to understand processes that lead to realistic images, and hope that we can eventually create realistic images with very little prescribed.”

As Reza points out, the current state of GANs lack what can be described in layman’s terms as true inspiration. They can create, but require a large degree of initial hand holding from developers — similar to other forms of machine learning.

“GANs have been overhyped, but that generative models (not necessarily adversarial) are very powerful”

Rachel has been working with GANs at fast.ai for the past couple of years, and has seen firsthand their potential vs the very public speculation regarding their ongoing capabilities.

“I think that GANs have been overhyped, but that generative models (not necessarily adversarial) are very powerful,” Rachel says. “While we have been teaching GANs since early 2017 in our fast.ai course (which has been taken by over 200,000 students), we have more recently focused on other generative models that provide equally good results, only much faster.

“For instance, in the most recent version of the course, we showed how to make blurry photos (such as the one on the left, which was input) more sharp (the algorithm output the one on the right). We did this first using GANs, and then without (which was much faster). The picture below (on the right) did not use a GAN:

“A downside to GANs is that they are brittle and slow to train. A fast.ai student, Jason Antic, has done some great work adding color to old photos in his project DeOldify:

“Jason uses a generative model that is not a GAN for this.”

Dispelling Hysteria

One doesn’t have to search very hard to find various news articles that showcase the results of GAN image generation, often accompanied with user comments ranging from the highly impressed to the deeply pessimistic regarding the technology’s potential for abuse. Granted, when looking at the generated images of fictitious people, their photo realism is obvious. However, just how big a threat do these capabilities represent, especially with the recent phenomenon of fake news, including allegations against state actors generating fake profiles on social media sites to spread misinformation?

“We have to be more careful to distrust images, that’s all. It’s not a big deal”

“The danger isn’t that big and is very overblown,” Reza says. “We have to be more careful to distrust images, that’s all. It’s not a big deal. It used to be photos could be vaguely trusted as evidence of something, but not anymore. As long as we educate the public on that, there’s not much danger, or any at all really.”

Technology capable of producing convincing doctored imagery is nothing new. Photoshop and other Adobe Creative Suite applications have introduced industry-quality levels of media creation into the consumer sphere, with the limits of their use often being said consumers’ imaginations. Convincing faked photographs, and edits of text/Twitter conversations have all become common sights across the digital sphere.

“There are dangers from generative models (not specific to GANs, which are just one class of generative models) around disinformation,” Rachel says. “Keep in mind that disinformation is already a huge issue, even when using primitive tools like simple memes and Photoshop. Russia in particular has effectively used disinformation to meddle in the 2016 election, to sow divisiveness, and even fuel measles outbreaks by spreading false info about vaccinations.

“Again, this threat is not specific to GANs, or even to images. Consider the concerns about how OpenAI’s new language generation model could be used to create computer-generated text at scale. As a society, we are doing a poor job of addressing misinformation (and how easily the major tech company platforms can be manipulated), and generative models can increase this danger.”

GANs may represent a new level of sophistication for these practices, but the underlying concept of faked imagery and video being widely propagated remains the same. All that is required is ongoing awareness of the matter. “As long as we tell journalists and other folks to distrust images from now on, I think it’ll be OK,” says Reza. “This is why I put time into speaking with journalists about it.”

The Positive Potential of GANs

The vast majority of media attention given to GANs focuses on their ability to produce faked representations of celebrities, or the generation of fictitious people for nefarious purposes. Of course, such technological capabilities are completely at the mercy of whoever is utilising them. To discount GANs’ potential for image manipulation on a more general scale, though, is to be short sighted about their potential.

“Generative models can be used to create text, summarise paragraphs, or answer questions”

“Generative models (not just GANs) hold a lot of creative potential to fix and enhance images (Adobe is investing heavily in deep learning), as well as to create new artwork,” Rachel argues. “In the area of languages, generative models can be used to create text, summarise paragraphs, or answer questions. There is also potential for generative models to be used to augment data (a technique that helps models to train more accurately on smaller data sets).”

The potential of GANs goes far beyond the realm of image manipulation. The broad nature of the underlying technology though leaves the door open to a wide range of applications.

“The idea of two competing neural networks helps us achieve more performant neural networks across many applications,” says Reza. “VR, AR, and Video game overlays can become much more realistic, and there’s also the idea of two competing neural networks which can be applied to other domains. That lets us generate audio and other content we’ve had trouble generating. So it’s quite useful.

“For me, the idea of competing neural networks is the most broad applicability, but a sense of excitement there is hard to convey to non machine learning practitioners.”

Developing neural networks to function in symbiosis with one another is an important step towards developing a new category of artificial intelligence — one that displays the ability to modify its both approaches and responses to a set task. The concepts of teaching and learning require a level of independence from one another, and therefore a level of reasoning. While the direct methodology of GANs at present are still trial and error, there is scope for the adversarial aspect of the technology to increase in complexity.

The Next Step on from GANs

GANs have very much captured the imagination of a large portion of the machine learning community — with their current applications able to generate tangible, visible results that can be shared by those outside of the machine learning field, it is easy to see why. We asked Rachel and Reza what they saw as the most exciting area of machine learning research beyond GANs.

“New research is being done to achieve the same results as GANs with generative (non-adversarial) models, which are often much faster”

“I think the next breakthrough will be ML models that unify interaction with the physical world with vision and language systems,” Reza says. “Tasks like grabbing objects using a robot arm, self-driving cars, and walking/running simulations are all seeing good progress and are likely to see a big increase in capability soon.”

Rachel adds that, “New research is being done to achieve the same results as GANs with generative (non-adversarial) models, which are often much faster. Beyond that, natural language processing is currently going through an explosion of advances (similar to where computer vision was a few years ago).

“Fast progress is being made on generating text, classifying text (e.g. is this review positive or negative?), translation between languages, question answering, text summarisation, and more. For instance, see ULMFit, BERT, and GPT-2 which all came out in the last year.”

Machine learning is rapidly continuing its advancement, and breakthroughs like GANs put the larger field under an intense amount of public scrutiny. The creation of deepfake videos are just one aspect of a potential huge tidal shift in how privacy and personal identity are protected and commoditised. The question is, will legal frameworks need to drastically change to cater for this?

“Legislation has really lagged behind in keeping up with the huge impact and influence that major tech companies are having on our society,” Rachel says. “I think that Anil Dash’s framing is helpful: there is no ‘tech industry’ as that label is so broad as to have lost meaning, as more and more industries use technology (and the major tech companies are involved in so many different fields).

“We should focus on regulating specific use cases: such as how algorithms can discriminate in hiring, firing, and criminal justice decisions; how social networks promote extremism and even genocide; how police departments are using facial recognition technology; and increasing surveillance and lack of privacy. I think it is helpful to frame this around what human rights we want to protect.”

Deepfakes Will Not Ruin Society

The fear over the potential applications of deepfakes has been widespread and, in many cases, hysterical. A future in which no political speech can be trusted and smear campaigns are spawned in the imagination of deepfake programmers is fanciful — so far the faked videos have simply added noise to the conversation rather than diverting it. Those who already hold certain views will want to believe that the Pope endorsed Donald Trump, for example, but the fakes are too easily spotted for most people to be duped.

One reason deepfakes haven’t shaken our political system too violently is because they are fairly straightforward to track. Machine learning algorithms can identify doctored video and, for trolls, time can actually be bettered used disseminating lies that won’t be picked up by an algorithm. So far, the most damaging uses of deepfakes have been in pornography, splicing the faces of famous people onto the bodies of porn actors. Of course, this needs to be addressed and dealt with, but there is little evidence that deepfakes will have any real impact in politics. Forged photographs, for example, are not a political force. Deepfakes are impressive and uncanny, but not terrifying.

John Murray is a tech correspondent focusing on machine learning at Binary District , where this article was originally published.

Why The Danger of Deepfakes Is No Danger At All was originally published in Primalbase on Medium, where people are continuing the conversation by highlighting and responding to this story.

Everyone wants to come up with the next big thing. If you’re lucky enough to succeed in this, you’re soon faced with an uncomfortable realisation — everyone will want to steal your idea. This is the nature of innovation and commerce. It’s often said that imitation is the sincerest form of flattery. Those who have seen their hard work and personal vision pilfered by others may not quite agree with this sentiment, however.

Patents offer a safeguard for intellectual property against potential infringement of every level. In the world of tech, patents are big business. Speculative articles concerning new features in the yearly iPhone refresh cycle are fuelled by new patents filed by Apple, with commentators publishing vague blueprints obtained from the patent office’s public records, and deducing that the next handset will invariably see some incorporation of these often vaguely-worded concepts.

Obviously, giants like Apple are expected to vigorously protect their latest technological developments from competitors. Bt what about tech startups? In a crowded marketplace dominated by Silicon Valley, Chinese, Korean and Japanese firms, shouldn’t the smaller firms also act to protect their R&D?

What tech startups need to be aware of right off the bat is the competitive patent arena that they are stepping in to. The major tech companies have dedicated departments with massive operational budgets that facilitate dizzying numbers of patents per year. In a report by the Intellectual Property Owners Association, IBM had a staggering 8,996 patents approved in 2017 alone.

Startups are unlikely to have access to either the funding nor legal manpower to compete. Rather than take a scattergun approach to patent filing, smaller companies must concentrate on smaller, quality patent filings that, should the startup want to pursue this avenue, leave the door open to potential cross-licensing agreements.

The reason why patent law is so lucrative is because it is so complex. While it may seem like a no brainer that startups should immediately file patents in order to safeguard their ideas, the reality is one of the key considerations that founders need to make before rushing in to the patent office.

Startup Products and Goals Change

A revolutionary product is unlikely to remain the same as it journeys from inception to market. A product will likely go through numerous iterations before it is ready to consumer adoption. Paul Graham, co-founder of Y Combinator, once pointed out that at least 70% of startups have a different core idea or product at the centre of their business within 3 months. Dedicating time and money on securing the rights to intellectual property that likely won’t be pursued in its current form is counterintuitive, and could deplete already scarce resources within the company.

Time and Money

There is often a mistaken belief that once a tech company has developed an idea that can be patented, there is a rush to file the patent and quickly secure it. Unfortunately, in the US at least, the process is more costly and drawn out than many people imagine. Patents deemed as ‘extremely simple’ have been estimated to cost $6000, while entire cases of patent filing and prosecution can increase to upwards of $60,000.

In 2016, the United States Patent and Trademark Office estimated that the average wait time between the filing of a patent and the first office action was up to 16 months. The wait, and money spent on the process, could be seen to be a necessary investment, but the success rate of actually getting a patent granted in the US is surprisingly low too — a study from Yale in 2015 found that only 56% of applications were approved.

Reverse Engineering and Foreign Competitors

What constitutes an ‘idea’ is so broad that the legal process of filing a patent does not necessarily preclude competitors from finding ways around it. Patents are public record, and there is the possibility of third parties finding a piece of protected work and copying certain key aspects of it — just enough to skirt the confines of direct infringement. What results is a product that offers the same kind of monetisable functionality, but without any kind of compensation to the original startup.

Not only this, but patents filed in one country, e.g. at the U.S. Patent & Trademark Office, are not necessarily protected from infringement overseas. A great deal of media attention has been given to fragrant patent violation by Chinese companies, including that by several prominent mobile phone manufacturers.

Precedent

The tech industry has unfortunately seen important case law, in the US at least, in which big tech companies have been far better favoured in disputes over patent infringement than the startups going up against them.

One example is the case of eBay Inc. v. MercExchange (2006). eBay were using key functionality on their site, including the ‘Buy Now’ option across their auctions, of which MercExchange owned the patents but were allowing eBay to use through a license agreement. When eBay abandoned plans to purchase the patents, MercExchange sued them for patent infringement. In a series of reversing decisions throughout the courts, the Supreme Court eventually ruled in eBay’s favour, allowing them to continue using the intellectual property throughout their site.

Fast Execution

The tech market may be rife with opportunistic poaching of ideas and products, but the market also rewards rapidly executed ideas that capitalise on consumer need. Don’t file a patent before actually getting a product that’s ready for market. Instead, tech startups should consider the benefits of establishing themselves as the market leader in an area first.

Facebook, Airbnb and Google approached the market in a similar way in the past when they were in their smaller stages as companies. They established market share by quickly rolling out new products and developments, securing investment, and consumer recognition before focusing on patents at a later stage.

Alternatives

Patents are not the be-all and end-all in protecting a tech startup’s intellectual property. Founders can look to several quicker, cheaper and more easily attainable forms of safeguarding their work, starting off with the basic first steps such as registering trademarks, and even .coms and usernames on major platforms to prevent domain camping by individuals hoping to make a profit out of these free assets.

Another vital step for startup founders to take is to draw up non disclosure agreements. These prevent staff from poaching intellectual property from the company, without the cost and legal time consumption that the patent process entails.

The Potential Patent Pitfalls was originally published in Primalbase on Medium, where people are continuing the conversation by highlighting and responding to this story.

If you’re reading this on your phone, what make and model are you using? If it’s any leading smartphone from the last couple of years, more likely than not you’re making use of FaceID to unlock the screen, and it’s a pretty seamless experience.

Yet while it is now enmeshed into our everyday lives and may seem to be a fully-developed technology, the wider use of facial recognition technology poses a significant threat. Such technologies could identify any person for any number of reasons, which is opening the door to a number of problems drawing widespread attention. There are real problems, and these are not just in the moral implications of how people intend to use the technology — the real danger may be that the technology is not even actually ready yet.

Inaccuracy

Efficiency in facial recognition would, on the surface at least, appear to be largely achievable. My own use of my iPhone’s FaceID works like a charm in a fraction of a second (minus those initial morning unlocks in bed where my face is contorted by squinting at my screen). This success rate is because my face is the only one stored on my phone. There is no database being referenced against — it’s just me that has to pass the Face ID test.

Move beyond the closed wall ecosystem of an individual phone though, and the problem of inaccurate facial recognition really becomes apparent. Facial recognition systems are only as good as the quality of data being fed into them, and this quality is woefully lacking in many systems that are being rolled out today — most notably, in law enforcement platforms. The Metropolitan Police used facial recognition at the 2017 Notting Hill Carnival in London, and reported a 98% failure rate, with 102 false flags at the event.

The FBI has reported better success rates, but not at a level that should inspire sufficient confidence in a system being used to identify suspects in cases that require absolute confidence. In a 2017 Congressional hearing, Kimberly J. Del Greco, Deputy Assistant Director of the Criminal Justice Information Services Division of the FBI, stated that the bureau’s systems had only achieved an 86 percent rate of accuracy.

When the issue of racial identification comes into play though, the current shortcomings of facial recognition systems become even more apparent. A report in 2018 from MIT and Microsoft researchers found that facial recognition systems performed well on white men, but were far less reliable when identifying women and people of colour, with darker-skinned females being the most misclassified group, at 34.7 percent. A theorised root cause of this issue goes back to the quality and diversity of the images being fed into these systems, which is a larger problem within the machine learning research and development community.

A Lack of Regulation and Oversight

Despite the lack of diversity in training data being used in facial recognition system, there is no shortage of data itself. Governments’ and private companies’ recognition of the power and potential of these systems has been one of the catalysts for their rapid development. As a result, there has been a lack of cohesive regulation along the way, leading many to see facial recognition development as a Wild West landscape.

A 2016 report by the Georgetown Center for Privacy and Technology revealed that US law enforcement had been harvesting images of American citizens to utilise in facial recognition systems, while this year the FBI revealed that it held over 640 million photos in its database. The UK has suffered its own problems too thanks to this lack of regulation. The Police National Database hosted 13 million faces in 2014, including various individuals cleared of committing any offence. Furthermore, a 2015 Home Office report concluded that up to 40% of these images were duplicated, which paints a picture of a badly constructed, and potentially dangerous system being utilised by a police force.

The Rapid Spread of the Technology

A lack of regulation in the facial recognition sphere inevitably leads to a lack of solid figures concerning how many functioning platforms there already are, and whether or not these share databases. There are public indications about the crossover between public and private entities collaborating in this field, such as the recent story of Amazon shareholders’ attempts to veto efforts by the company to sell facial recognition technology to US police forces, but smaller companies and startups operating in the field are far less scrutinised.

The technology necessary to create facial recognition systems has decreased in price in the past few years, coupled with an increase in attainable computational processing power, and the ease in which training images can be acquired. Thanks to these factors, it has been estimated that the market for facial biometrics will reach $375 million by 2025.

As the technology continues to spread at a rapid place, the aforementioned problems could increase in scope, unless more considered oversight is established. Facial recognition is already being brought in to airports and schools, with Chinese iterations even being linked to the country’s controversial ‘social scoring’ system.

The positive potential for facial recognition also continues to grow, but there is a real need for a greater recognition of its importance in society by all parties concerned — from private citizens who may not be aware of the level of government investment in it, and from governments themselves who should appreciate the need for more concerted standardisation of their systems.

Why Facial Recognition Problems Can’t Be Ignored was originally published in Primalbase on Medium, where people are continuing the conversation by highlighting and responding to this story.

Everyone wants a piece of the AI gravy train, and the marketing industry is no exception. Studies have shown that 80% of enterprise-level organisations have integrated AI in some form into their business, with 32% of these being marketing companies.

The concept of AI is an appealing one for marketers — identifying customers using their previous purchases to create a smart profile of an individual is something that has already been portrayed in science fiction. In Minority Report, Tom Cruise’s character has his iris scanned upon entering a Gap store, and then receives a personalised holographic sales pitch based on his sales history, where he is offered tailored suggestions of other products in stock.

The whole scene may have seemed very intrusive back in 2002, but today, is this concept so far fetched? Smart assistants such as Alexa are giving marketers a glimpse of the potential for consumer demand in this field, but unfortunately, there are currently key barriers in place that prevent the wider marketing industry in fully exploiting AI.

Insufficient Infrastructure

Any AI-powered marketing strategy requires a comprehensive technology infrastructure to support and power it. This is something many marketers are unprepared for, and often unable to produce. Machine learning algorithms are only able to derive useful insights through the processing of vast data streams, which requires substantial hardware and processing power. Securing the necessary budget for all of this can be challenging for SMEs. They also require the data to have been collected in the first place, which turns it into a chicken and egg situation.

As AI development continues, and the established AI platforms develop cloud-based offerings and AI-as-a-service, the need for physical infrastructure for marketers may decrease, with the rise in becoming a more tenable possibility.

AI Skills Shortage

The expense of buying and installing the IT infrastructure is only the beginning — what follows is frequent updates and maintenance, which also goes hand-in-hand with the requirement of well-trained support technicians.

Demand for technical roles in AI development is skyrocketing, resulting in a noticeable AI skills gap. AI experts are now in a strong position to negotiate large salaries, and often have the luxury of companies approaching them and becoming embroiled in bidding wars to secure them. Marketing departments’ budgets may not stretch to the large salaries being negotiated, or have the manpower to dedicate to participating in employee bidding wars between hiring firms.

Even if a company has opted for a bought AI solution over an in-house build requiring these expensive AI experts, there is still a need for training to use and deploy this technology, which must be factored in to the marketers’ budget.

The Data Issue

AI is built upon access to high quality, copious data streams. Despite being in the era of Big Data, there is still a large amount of confusion and disorganisation amongst marketers in how to effectively utilise the data streams open to them. In a 2016 study from IBM, 54% of marketers interviewed declared that their ability to act on insights derived from customer data was ‘poor’ or ‘very poor’.

Marketers may have access to data, but it’s not necessarily of the right sort. Machine learning models require data sets that are comprehensively curated and ‘cleaned’, which is a skillset that the average marketer may not have, and another indication of why data scientists are in such demand, with their role being named ‘Best Job in America’ in the 2019 Glassdoor roundup).

Acquiring data is a process that is subjected to a great deal more scrutiny now too, both legislatively and in the broader public consciousness. GDPR places larger restrictions and the prospect of severe penalties on companies who fall foul of it, while recent stories such as the Cambridge Analytica scandal in early 2018 have injected a new sense of data awareness into the general public. All of this makes the concept of casual data collection that is easily up to code for machine learning algorithmic processes far less attainable to casual participants.

The Evolutionary Nature of the Marketing Industry

Marketing is a profession that has rapidly evolved over the past twenty years. The rapid progression of the digital age, with the birth of online commerce and social media among other factors, has necessitated the formation of digital marketing, SEO and SEM. In an age where data collection is possible to construct AI platforms, there is no guarantee that the models implemented to train them will remain viable and functioning forever.

When IBM began to utilise its AI platform Watson to manage its programmatic marketing campaigns, they reported an average cost-per-click reduction of 35%, with some instances seeing drops of 71%. Watson’s ability to achieve this was through its analysis of customer data, including their browsing habits by time online, and device used.

However, as GDPR and related changes to data collection and utilisation have illustrated, consumer behaviour and access to data sets changes over time, which makes the prospect of AI platforms having access to reliable and consistently high quality, specific data streams difficult to guarantee.

Marketers need to factor in how their AI usage can evolve with their industry, to ensure ongoing efficacy.

The Struggle of AI Marketing was originally published in Primalbase on Medium, where people are continuing the conversation by highlighting and responding to this story.

Trade shows and expos have historically been crucial calendar events for businesses of all sizes. They are a great opportunity to showcase your products on a large scale, attract investment, and secure new customers. But are you making full use of them?

Even in an era of online marketing and virtual stores, trade shows and expos still play a vital role across almost every industry, and none more so than tech. Online marketing techniques such as promotional videos will only go so far — there will always be a need for face-to-face demonstrations, allowing prospective customers to get hands-on experience with the product.

Today, the expos market is bigger than ever — worth an estimated $14 billion, according to IBISWorld. There is a forecasted growth of 2.8% in the industry for 2019, making expos a crucial avenue of outreach operations for startups.

Here’s how you can make the most of them.

Finding the Right Expo

A huge variety of tech expos exist all over the globe, from established giants like Web Summit that cover every technology under the sun and attract Fortune 500 companies, through to more specialised events such as the AI & Big Data Expo.

Finding the right expo for your startup is key. Don’t just look for one that caters for your general industry — research past attendees and exhibitors to discover if the expected traffic for this year will align with your intended market audience. Do exhibitors come back after previous years? If they didn’t, it may well be because they didn’t see any returns.

Factoring in the Cost

There are many initial costs that go into securing an exhibitor place that must be considered. As a startup, it’s likely you are working with a limited budget, and research has shown that B2B companies spend on average 39.2% of their annual marketing budget at trade shows.

It is not just the rental of the booth space at the expo that commands expenditure, but also the cost of travel for team members attending the event, including hotel bills and food, along with marketing your presence, branded merchandise at the event, and so forth.

Be aware of hidden costs and don’t just spend money for the sake of it, don’t waste money on crap merch — either don’t bother or spend on something people might actually take away. Really consider how you promote your presence too. You want to drive as much traffic to your stand as possible and it’s worth spending that extra bit to let everyone know why they should come and talk to you and how.

Take the Right People

A stylish product display is great, as is running a pre-built demo for visitors to your stand to observe. More important, however, is to have people who really know the product inside and out.

Attendees will likely come from a large array of professional backgrounds, with varying levels of technical expertise. Developers will be keen to ask more informed questions about the foundations of your product, making it prudent to have people on-hand from your development team who can answer such inquiries.

They don’t only need to be technical enough to answer questions either. They also need to be personable enough to generate sales leads, and have the savvy to build database numbers through signups. Not an easy find!

Get Immediate Feedback

Expos offer a superb opportunity for startups to test out their products/services with attendees. Developers can see first-hand how people interact with the UI, which can aid in tweaking the product. The questions asked can help developers gain a better understanding of potential new features to add. There is also an opportunity to gather product testimonials which can be used in future marketing campaigns.

Networking and Competitors

Working non-stop on a product in a startup can be an isolating experience. Expos provide the ideal setting for startups to observe competing companies in the industry, including how they position their products, and the type of considerations that they have made in regards to UX and design. This is not industrial espionage by any means, but rather utilising public consumer showcasing to get a better idea of how others have approached their products. Your startup’s presence alongside competitors will also highlight you as an alternative for consumers looking for choice in the market.

Of course, fellow attendees at expos will not all be competitors. The diversity of industries like tech, even in segmented areas like AI and blockchain, means that expos provide an additional opportunity of networking with other startups. Complementing solutions can be found between companies’ products or services, and trade events give developers and designers a fertile environment to make connections that can benefit all parties concerned.

The variety of expos all over the globe provides startups with ample opportunities to bring their products to the attention of an international audience. Of course, no startup should rush their development cycle to meet the deadlines of such expos, as showcasing a buggy or plain unfinished offering can irreparably damage that company’s reputation. However, with careful research, budgeting and logistical planning, an expo exhibition can be a real boon to startup’s public presence and ongoing product development.

Exhibiting Your Startup at an Expo was originally published in Primalbase on Medium, where people are continuing the conversation by highlighting and responding to this story.