From voice assistants to self-driving cars, artificial intelligence and machine learning are overtaking every aspect of the industrial sector. Machine learning algorithms are used to automate laborious tasks in businesses to discover patterns in existing data without being explicitly programmed.

The field is continuously evolving and high-value predictions are being used to make better decisions in real-time without human interventions. Under recent circumstances, investments in machine learning companies have drastically increased. Analytics Insight presents the top machine learning funding and investments in Q2 2021.

Lambda Labs

Amount Raised: US$15M

Transaction Type: Not Specified

Key Investor(s): Bloomberg, Patrick Collison, and others

Lambda provides deep learning services to top tech companies like Apple, Microsoft, MIT, and others. It is an AI infrastructure providing computation to accelerate human progress.

Zeitworks

Amount Raised: US$2M

Transaction Type: Not specified

Key Investor(s): Madrona Venture Group and Jazz Venture Partners

The company’s SaaS platform leverages machine learning to discover, analyze, and improve clients’ business processes without interfering with users or requiring system integrations. Their solutions measure the problems faced by the clients and execute step-by-step processes to solve the issues.

Deep Instinct

Amount Raised: US$67M

Transaction Type: Series D

Key Investor(s): Chrysalis Investments

The company uses deep learning algorithms to provide cybersecurity services. Deep Instinct’s on-device solution protects against zero-day threats and APT attacks with unparallel accuracy. By applying deep learning technology, enterprises can gain unmatched protection against cyber threats.

Physna

Amount Raised: US$56M

Transaction Type: Series B

Key Investor(s): Tiger Global, Sequoia Capital, and others

Physna is a geometric deep-learning and 3D search company that focuses on comparing and analyzing 3D models. The company powers innovation in manufacturing by bridging the gap between physical objects and digital codes.

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Start-up ecosystems thrive in the UAE with a boom in Big Data

A company or a business traditionally starts from scratch. From planning to finance to management and execution, start-up ecosystems have faced challenges to establish themselves. Today, the business world is productive, a lot more supportive, and believes in two-way progression. Tech influencers and game-changers across the world are setting the trend of helping entrepreneurial ecosystems thrive. Microsoft supports the fields of tech and business to flourish in the emerging new area of Big Data at Hub71.

The world lives and runs on technology, it exists and procreates over technology. Technology on the other hand is full of Big Data. This data has now become “the new oil” for start-up ecosystems. Recent Global Data research cites the examples of 3.5 billion Google searches and 1.3 billion internet-of-things connections per day, and 254 Exabytes (one Exabyte is a billion gigabytes) of mobile broadband data traffic per year. Big data is a combination of structured, semi-structured, and unstructured data collected by organizations that can be mined for information and used in machine learning projects, predictive modeling, and other advanced analytics applications.

The digital data explosion began in 2010 and has advanced remarkably. Statistics predict it is increasing even more up to 26% by 2022. Big data technologies help companies store large volumes of data while enabling significant cost benefits. Such technologies include cloud-based analytics and Hadoop. They help businesses analyze information and improve decision-making. Furthermore, data breaches pose the need for enhanced security, which technology applications can solve.

The Middle East nations on the other hand are escalating increasingly in terms of health tech, big data, property tech, artificial intelligence, etc. It has become a home for start-up ecosystems and tech in contemporary times. Magnitt, a start-up data analytics company states that more than 60% of investment funding for start-ups in the MENA region was given to the projects based in the UAE in 2019.

The global tech conglomerates like Microsoft, Google, Facebook, and Twitter have already established their MENA Headquarters in the UAE. Hub71, which is located in the heart of Abu Dhabi, has built a niche between the well-established companies to those of budding ones. Its networking capability, its accessibility in terms of capital, communication have all paved a way for Hub71 to grow as an emerging global marketing giant.

“There is a strong start-up culture and plenty of advice available for young entrepreneurs looking for their next move into the Middle East. Microsoft is a leading light in the big data industry across seven technology segments, including data security, storage, processing, aggregation, and integration. The UAE has become a hub in many areas, including technology” stated Sayed Hashish, general manager for Microsoft UAE that has been operating its services from the past 25 years as the strategic partner for Hub71.  He also adds that there is a lot of potential for start-up culture in the UAE for young aspiring entrepreneurs. Microsoft on the other hand, is a torch bearer across seven technological segments, including data security, storage, processing, aggregation and integration.

Hashish said that Microsoft has always believed in focusing on start-ups as its core value. He also mentioned that Hub71 was launched with the central theme of encouraging and establishing start-ups. The timing was absolutely perfect in his view.

Another core mission of Microsoft was to empower every business to turn the game around the planet and Microsoft has already fulfilled that. Apart from building huge data centers operating in the region, the company, which is the only one in the US to still be worth more than US$1trn after the coronavirus pandemic, has made plenty of time to mentor newer companies.

Microsoft and its services have always been incredible in many ways. Atif Mahmoud, Founder of British edtech start-up Teacherly said that opening the company’s second office in Abu Dhabi (the first was in London) had given his start-up the boost it needed. He then said that the tech giant joining hands with his once- upon time small start-up was a dream come true.

Putting it in a nutshell, Abu Dhabi’s Hub71 literally is reigning in the global tech-business industry with loads of opportunities for aspiring young entrepreneurs wishing to establish start-ups to flourish in Big Data and Microsoft is doing all it can to accelerate the global industry upwards.

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Artificial Intelligence will have a great impact on many aspects of our life, including IP protection. Investments on AI development are increasing and strong competition amongst various countries already started, with USA and China leading the path. EU is also planning investments and study researches, including a call for tender relating to “Trends and Developments in Artificial Intelligence – Challenges to the Intellectual Property Rights Framework” launched on March 2019. As we will see, current copyright laws in EU are probably not covering AI results. The question is whether protection is needed; and in this case what kind of protection would be more advisable, taking into consideration the effects – and the possible counter-effects – on human authors’ protection and the economy.

     1. Results produced by AI

Examples of works of art created by AI are now numerous. One case concerns the Collective “Obvious”, based in Paris, that used the IT system GAN (Generative Adversarial Network) to produce a portrait in a pictorial style between the 14th and 20th centuries. The goal was to produce a portrait that could not be distinguished by a work created by a human author, with reference to style and subject, and at the same time “original” (in the sense that it was not identical to prior paintings). The result was a work of art named “Compte de Bellamy”, that went to auction at Christies in October 2018. Similar examples relate to the songs realized by the “Creativity Machine” of Dr. Thaler, that in one single weekend produced 11,000 new music tracks. In these cases, the natural person/s collect the data input and expose the IT systems to such data, choosing the goal of the activity, however without making any decision as to the actual expressive form of the final work of art. It is the IT system that through a generator elaborates various results; such results are then selected by a second device (the discriminator) based on their indistinguishability from a work of human creation and on the basis of their “originality”.

     2. Creation in Copyright

Works of art are protected since they are the author’s own intellectual creation, i.e. the result of the choice, sequence and combination realized by the author (see ECJ 16th July 2009, C-5/08 – Infopaq). As indicated above, AI works cannot be distinguished by human creations, is so far as they look like them. Therefore, in judging from the features of the work, there should be no difference between AI and human creations, also considering that the threshold for the protection of the latter is generally considered to be rather low. However, there is a key question here to be answered, i.e. whether creation as such implies conscience and will. If this is the case, AI works could not qualify for the protection, as the IT systems could hardly be said to exercise conscience and will for the creation.

     3. Author as a natural person

Another issue concerns the possibility for a non-human entity to acquire the copyright on a work of art. According to the vast majority of copyright laws in the world, only natural persons can be authors, and acquire both economic as well as moral rights. The latter can hardly be conferred to a device. In this respect one could make reference to the debate originated in the USA in relation to the famous “Naruto” portrait, that was a photograph taken by a macaque, using the camera let on purpose unattended by the owner, the photographer David Slater, to allow spontaneous activities by the group of monkeys that he was observing in 2011. The photographer claimed to be the owner of the photograph, however the United States Court of Appeals of the Ninth Circuit found differently with its decision of April 23, 2018. In 2014 the US Copyright Office expressly established that copyright can only protect original works of authorship, i.e. created by a human being, excluding works created by “nature, animals, or plants”. Therefore, it would seem that AI works autonomously produced by the IT system could not qualify for copyright protection. In other words, protection could be granted only when there is human intervention in the process carried out by the AI. After all, one has to bear in mind that the approach toward AI should be based on the key principle according to which the center and focus of the protection is and remain the human being. Therefore, AI is a tool and should not be the goal or the focus of the protection.

     4. If AI cannot be protected by copyright, is there a need for another form of protection?

If AI cannot be protected by copyright, as indicated above, the fear exists that this will result in decrease of investments in AI, thus limiting the possibility of quickly reaching all the positive effects that AI seems to promise. For this reason, some countries are going far and debating the possibility and opportunity to even granting legal subjectivity to AI (for instance, Saudi Arabia would have granted citizenship to a robot named Sophie, while Tokyo would have granted residence to another robot called Shibuya Mirai). Setting aside these extreme positions, that would require careful examination of many issues (first of all, ethical and philosophic issues), it would seem that there are a number of options available to try and protect AI’s results. For instance, one could reconsider the notion of creativity, and extend it to comprehend works created by AI, enhancing human intervention consisting of selecting the data collected and entered into the machine and choosing the parameters that define the objective of the machine’s activity. It is also possible to think of attributing a kind of sui generis right to AI (rather than copyright), also given the circumstance that the value behind AI seems to concern more the investments than the creativity.

     5. “Killing” human authors?

Finally, it has to be considered that AI produces (and will increasingly produce) high amounts of works that will not be distinguishable from works created by human beings, as indicated above. One certainly shares the view that AI should be used to improve wellbeing for human beings, rather than the other way around. Therefore, it would seem rather reasonable to try and maintain high protection for human beings when they create works of art, and exclude any protection, or grant minor protection, to the results of AI activities. However, it is necessary to also think to possible countereffects of this approach. Works created by human beings, albeit substantially similar to those created by AI, will present higher barriers to exploitation than the latter, that would be in public domain, or subject to a shorter deadline for the protection, or in any event probably cheaper. Would this benefit and promote human creativity, or will there be an opposite effect? In this scenario, a possibility is that the notion of creation (and the relating copyright protection) is limited to those works of art realized by a human being, which are the result of an intuitive creative act, not reproducible by a machine. However, since creativity and its nature has been quite a complex issue to define (and still is), one wonders whether it is actually possible to draw the line above indicated. Also, it should be carefully consider how this conclusion will impact to the job market in the creative industries (setting aside the circumstance that AI will probably dramatically change the way through which such industries will operate).

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Along with autonomous cars, virtual reality, and drones, artificial intelligence (AI) has been one of the key areas of tech that is generating huge amounts of excitement. And rightly so.

From the much hyped triumphs of Google’s DeepMind, to those very handy recommendations on Skype and Netflix, us regular human folk are only seeing the beginnings of what AI holds in store in the next few years.

AI is also taking huge strides in helping people manage their finances, which is particularly important when looking at the state of savings in the UK.

The figures are pretty grim.

This year the savings ratio, which is the amount a household saves as a proportion of its income, reached its lowest level since records began.

During the mid-nineties the figure hovered around the 15 per cent mark, but for the first quarter of this year, it stood at just 1.7 per cent.

If you can’t face more scary financial figures, look away now. There are a staggering 16m people in the UK with less than £100 in savings. And by 2050, it’s estimated that our savings gap (the difference between what we will have at retirement versus what we need) could be as high as £350bn. Ouch.

Tackling the savings struggle

There’s a multitude of factors at play here. More recently, Brexit has really slammed the breaks on the economy’s growth, interest rates are still hovering at record lows, while spending in recent years has got easier and easier.

We now live in a world where you can order pretty much anything with just a few clicks, and before too long these will be delivered straight to your door by one of Jeff Bezos’ army of Amazon drones.

Whether it’s the pressures of social media, the realisation that you’ll be lucky to ever afford more than a cardboard box in London, or just the temptations of avocado on toast, millennials in particular are struggling to save.

So let’s explore how AI can help you with your finances.

Making saving less dreary

Advances in technology can make the boring task of savings a little less tedious. Using a chatbot – which is a computer programme that can have a conversation with humans – can help encourage people to engage with their finances.

The magic lies in the algorithm, which to put it simply, looks at your historic transactions and builds an accurate picture of your usual spending and income.

Using this picture, you can squirrel away small amounts of money that the programme estimates you won’t need by the end of the month.

The data can be refreshed and recalculated daily to make sure you can adjust to any new circumstances that might arise.

Detecting savings opportunities

AI is applied to learn your transactional patterns. Basically AI develops an understanding of users’ regular spending patterns, and is able to adapt quickly and learn from any changes.

Most people’s spending usually follows a similar pattern month-to-month, but when changes happen the programme can adapt.

For example, if you begin cycling to work daily, then an AI system can notice that TFL charges are no longer a regular occurrence and will increase your savings to account for this.

AI can also help you save money on your bills. For example, a users will be being notified if they are overpaying on their utilities. They can then be given the option to switch to a cheaper, greener alternative, which could save them hundreds of pounds a year.

Helping with budgeting

Making people aware of their finances is the first step in helping them gain control.

While AI-powered programmes will usually work in the background, it can give users a daily update on their bank account balance. Users can then simply click into this to see how their savings have developed daily, or view their week-on-week spend.

By subtly providing a way to keep track of their balances, users can quickly develop a sense of their spending, and are able to manage their finances more effectively as a result.

It’s also worth mentioning that users can set goals to save for. This helps them visualise and keep track of their savings progress and in turn makes budgeting simple and effective.

Managing investments

In a similar way to understanding spending habits, AI can be applied to understand an individual’s investing history and preferences.

It can match these preferences with investment products to offer a tailored investment portfolio which is unique to the user’s financial situation and risk appetite.

Artificial intelligence should also be able to help you decide when to buy and sell assets, preventing your emotions from influencing your investment decisions.

Predicting future spending

By building an understanding of a user’s transactional history, AI can be used to understand the relationship between transactions and other external factors, such as the time of year or month, purchases made when an account balance reaches a certain level, or when purchases happen in reaction to others.

By using this, and constantly analysing, AI is able to build a robust model of how users may act under certain future conditions.

Also, it is very easy to pick up on regular purchases and brand loyalty to help predict when and for what the user is willing to try new brands and products.

At Plum, our users save on average around £150 a month, and in many instances, without even noticing.

This is just the beginning.

Through AI, users can have their very own financial butler – an assistant that saves without you noticing, that monitors your bills and spending, and makes sure you get the best deal. All of this without you even having to lift a finger.

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