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GETTING SERIOUS ABOUT AI

Carmine Rimi, Product Manager AI and Kubernetes, Canonical – the company behind Ubuntu

 

Artificial Intelligence (AI) has witnessed extraordinary growth in business circles over the past few years. So much so that it feels an omnipresent buzzword in corporate technology conversations the world over. The advent of cloud computing and open source initiatives have supported the rapid expansion of these technologies, which are experiencing huge investment from both multinational businesses and smaller enterprises.

 

Deloitte’s Digital Disruption Index highlights that 85% of senior executives plan to invest in AI by 2020, while Stanford University’s AI Index reports a six fold rise in the annual investments from venture capitalists into AI start-ups since 2000, with significant quickening of that investment after 2010. Moreover, Gartner predicts that the business value resulting from AI initiatives will hit $3.9 trillion by 2022, rising from $1.2 trillion in 2018. These impressive figures serve to underline the huge potential AI boasts.

 

Carmine Rimi

The trend towards the adoption of AI shows no signs of abating. In a software dominated world, AI acts as a trigger for business growth, innovation and the launching of new, advanced services for consumers. However, the deployment of AI technology is not a straightforward process, and no industry is immune from this complication. There are many considerations prior to a business being able to enjoy the advantages AI can deliver.

 

 

Challenges ahead

Business have had to discover that rolling out AI technology can be problematic. From concerns around integration with current systems, to a lack of understanding around how AI works, it’s clear that there are broad and complex challenges. A recent report from Databricks, for example, stated that 96% of organisations are experiencing data-related problems such as inconsistent datasets, while 80% reported a lack of collaboration between data engineers and data scientists. Then we come to the question of compute power. AI solutions tend to leverage large reserves of processing power, which will rise as data volumes rocket and the algorithms driving these systems grow increasingly more complex. This presents some large concerns around scalability.

 

It’s important to note that, from a practical point of view, AI technology is very much in its infancy but is developing rapidly. While the technology has been spoken about for some time, it is only during the last few years that deployments have started to take place and increase.

 

Challenges to AI deployment can even emerge before the roll-out begins. One of the major barriers for AI is IT teams and business leaders understanding how it can be used for everyday business problems; and also, how it can fit to the specific requirements of the company. As opposed to deploying AI for the sake of it, companies should look at where the technology can make the biggest mark, and what specific processes would benefit from being automated. This is easier said than done. AI requires people with knowledge who can seize the challenge of turning the theory into profitable outcomes. AI encompasses a range of processes and technologies, such as machine learning, data transformation, model creation, natural language processing and deep learning. To derive the most value from these it is essential to understand the differences between these innovations. So, what does it take to deal with these issues to realise the potential of AI?

 

 

Making the most of AI

Capitalising on the power of AI comes down to a few key factors. Primarily, it’s crucial that companies understand the importance of rolling out back-end infrastructure and systems which can support the compute-intensive tasks involved in AI and machine learning. Operating systems then have to be adjusted to these sophisticated workloads, which allows businesses to work with huge datasets, deploy applications at scale and manage the complexity that comes with that. Getting AI systems operational takes a lot of time, effort, expertise and resources – which are not always at the disposal of an organisation. AI platforms are only as good as the people who programme them. The industry skills shortage can impact businesses, so it’s important to partner with experts who are able to guide them and address any internal gaps. Enterprises need to be considered in the way they introduce AI, creating a long-term strategy and investing in the right people with the right set of skills and experience.

 

Harnessing the power of AI may be easier said than done, but no one doubts it presents a phenomenal opportunity. A laser-like focus on how AI can be leveraged to solve business challenges will help. AI has been growing in intelligence for some time – now businesses need to follow that lead and realise the true potential of this remarkable technology.

 

 

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Technology

ARTIFICIAL INTELLIGENCE AND FUTURE OF TECHNOLOGY

Ashish Jain, CEO, Future FX

 

Artificial Intelligence refers to machine intelligence that is programmed to think like humans and mimic their actions. For example while writing this article, I am not actually typing it but dictating it out using the microphone and the text is being typed by Microsoft Word itself.

The ideal characteristic of artificial intelligence is to rationalize and take actions to achieve a specified goal.

As technology advances the previous methods of artificial intelligence are taken for granted as new necessities are conjured. For example the computer was one of the most iconic invention of artificial intelligence but now it is considered as mandatory.

Artificial intelligence is continuously evolving and has to evolve. Machines are made in a way that they understand mathematics, linguistic, psychology and many more other terms that are related to human mind.

Artificial intelligence is used in many sectors for example the medical sector. It is used to test drugs and medicines.

We have applications and games which includes chess where the computer plays against us this is also a feature of artificial intelligence. Similarly self driving cars are also an invention of artificial intelligence. These have to be designed very intelligently.

This can also be used in the financial industry to trace and flag activities in banking and finance such as unusual debit card activity or usage and large deposits.

This also helps to estimate the demand supply and prices of the estimates and that makes trading easier.

Earlier, we had to pay a visit to bank on order to deposit a cheque. Then we updated to ATM/Debit Cards and now you can be identified by your retina. Many different sectors have also adapted this method to make actions it more convenient and safe.

Some more examples of artificial intelligence are iPhone’s Siri, Google’s Smart Assistant, Amazon’s Alexa, Google Maps, Ride- sharing apps like Uber and Ola, diseases mapping, Automated investing, virtual travel booking, social media monitoring, inter team chat tool, NLP tools, etc.

Artificial intelligence is all around us and playing an active role in our daily lives. Every time we open our Facebook newsfeed, do a Google search, get a product recommendation from Amazon or book a trip online, we are using it immensely.

In the coming years, computers might match or even exceed human intelligence and capabilities on tasks such as decision- making, reasoning and learning, analytics and pattern recognition, visual acuity, speech recognition and language translation.

Smart systems in commodities, vehicles, day to day use objects will save time and effort offering us a more customized and comfortable future.

It will help the medical sector hugely in upgrading the medicines and treatments, inventing new ones which haven’t been found yet and making everyone’s lives more safer and healthier. A large number of data can be collected from person to person about their health and nutrition and thus changes can be made in the lifestyle.

Artificial intelligence will bring changes in the educational system making it more revolutionary and advanced.

Overall, every factor has advantages and disadvantages and artificial intelligence has it’s lot too. Considering all the advantages artificial intelligence will also affect the human decision making power, analyzing and rational thinking, lifestyle etc. It will make people lazier and will affect their creativity. It can also lead to unemployment due to increase in usage of machines.

Like everything has a balance, artificial intelligence needs to be balanced too so that we can enjoy it’s benefits without suffering the negatives.

 

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Finance

COULD COVID-19 BE THE CATALYST FOR DIGITAL TRANSFORMATION IN FINANCE?

AI

By Simon Bull, Sales Operations & Business Development Manager at Aqilla

 

We are all now living in a new ‘normal’ where working from home is no longer a luxurious ‘perk’ of the job, but an essential. In the case of many organisations, the transition to flexible, remote working was successful, albeit slightly bumpy. But there is one department that has found it more challenging to transition to the required standards of remote working – the finance department.

The finance department often gets left behind when it comes to digital transformation largely because it is so heavily regulated. And because of this, one of the biggest problems the finance teams face is that it’s sensitive data will likely be stored on a hardware server on office premises. If you look at how organisations update their software as they grow, it’s usually the finance department lagging far behind, or sometimes forgotten about altogether. This is because finance has complex requirements that can lead to the attitude of: if it ain’t broke, why fix it?

Up until now, most finance teams have overcome the challenges this situation presents, but with the repercussions of the pandemic still very much in play, the complications that go hand-in-hand with on-premise technology have been more noticeable than usual. As a result, COVID-19 is becoming a catalyst for a digital transformation in finance, or more specifically moving finance and accounting software away from traditional on-premise solutions to built-for-cloud services. But what are the advantages of this approach, and what should finance teams be looking for in a built-for-cloud solution?

 

  1. Simon Bull

    Cost: The Software-as-a-Service (SaaS) approach that is the basis of many of today’s cloud computing businesses generally offers customers a convenient monthly pay-as-you-go model. Given that all that users need to access the software is a desktop, laptop or smart device and internet connectivity, they can also save money on the server hardware that has previously sat in the corner of the office. Hint: compare pricing from several potential providers to make sure there are no unexpected extras before signing up.

  2. Service: Good cloud-based providers offer extremely strong levels of customer support and service. It should be very easy to get help quickly and conveniently, and they should be in a position to offer advice, identify problems and fix errors without undue delay. Hint: ask for references from existing customers or look for online reviews to assess their service and support capabilities. Also, carefully check their Service Level Agreement (SLA) to clearly understand where their commitments begin and end.
  3. Security: Established cloud providers offer high levels of security, data protection and backup services as part of their ‘as-a-Service’ package. Customers benefit from the protection afforded by security specialists whose job it is to prevent breaches and keep data completely secure. Hint: Check their security policies and consider talking to existing customers about their security track record.
  4. Compliance: Cloud providers specialising in the finance industry should have compliance at the heart of their product set. Hint: Check with potential providers about their levels of compliance and certification, particularly if you have specialised requirements.
  5. Ease of use: today’s built-for-cloud software services are built for purpose, with many offering a high degree of bespoke capabilities so every user can tailor it to their precise needs. This is in contrast to traditional software packages that can be far less flexible, forcing the user to work in a particular way that might not be ideal. Hint: ask potential providers for an online demonstration to check the way the services work meet your needs.
  6. Performance: In the early days of cloud computing, finance software was too basic for many professionals to consider. Today, there are many entry-level services, while others offer a comprehensive range of capabilities to precisely fit the needs of professional finance departments. Hint: evaluate the range of capabilities offered by a cloud provider, which should include areas such as: extensive analysis, proper periodic management and business calendars, multi-currency, multilingual and multi-company operation, full VAT handling International coding, tax and language flexibility, automatic reconciliation / bank integration, built-in key performance measurement, advanced search, selection and drill-down, document and image scanning. Hint: compare the features of different providers in advance – if anything important is missing, look elsewhere.
  7. Regular updates: Software developers find it much easier to update and improve their services when they are delivered online, and can more effectively keep up with finance best practice and changes to rules and regulations. Many also encourage users to suggest improvements or new features which are then provided to customers at no extra cost. Hint: ask providers about how often they update their software and whether you can suggest improvements.

 

For many businesses, these are compelling reasons to adopt cloud-based finance software services, even in normal circumstances. But considered in the context of the current remote working environment, built-for-cloud finance software can help departments to adapt and capitalise on working from home and match the levels of digital transformation seen across many other key business functions.

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