I am a software engineer who has taken an interest in machine learning about a year ago. I work on a couple of projects where a large amount of data is collected, and am wondering what I can do (or practice) in order to start recognizing opportunities to implement machine learning. Until now, I've spent some time building various classifiers with tensorflow, caffe, etc. using pre-defined data sets for random things I've found online (whether it be photos, spreadshets, etc) - and I just want to be able to use it at work. What is the best way to start getting in the frame of mind where I start recognizing where I can apply it? I found this article: https://hbr.org/2017/10/how-to-spot-a-machine-learning-opportunity-even-if-you-arent-a-data-scientist but it didn't seem too helpful to me. If there are any other thoughts and/or resources online that you could point me to, that would be helpful. Thanks.
If you are trying to see whether machine learning can contribute to your work, first thing you ought to do is to see if any of the following tasks could help the business at hand. Machine learning tasks include the following:
- Density estimation
- Dimensionality reduction
AI training and readiness within your workplace
First and foremost, working within a company is definitely a team sport. You might have embraced the right attitude and trained yourself on AI adoption, but most likely your co-workers have not. Here lies the first opportunity. I suggest you take the initiative to organise for expert talks, workshops and trainee-ships on AI and Machine Learning so that your organisation can posses the necessary skills it needs to implement AI across all departments. My advice is that you co-ordinate with managers within your organisation to provide employees with the opportunity to pursue learning and training programs to enhance their careers and help them understand the applications of AI within the workplace. Coursera have an attractive package for enterprises that I would recommend for this.
Hackers have adopted increasingly more sophisticated methods in a bid to compromise sensitive data, intellectual property, digital assets or any aspect that can harm an organisations reputation or business continuity. Last year major organisations such as NIH (UK) and WPP reported that they had been struck by a major ransomware cyber-attack! Cyber security is one of the niche's that can benefit immensely by leveraging AI. Deep learning has been used successfully to detect the red flags of a cyber attack. My advice to you as a software engineer is that you consider looking at algorithms that can safeguard against network intrusion in your company and implement them. Alternatively, you can adopt cutting edge security software from startups such as deep instinct, BluVector and Splunk. This will put your company at the forefront of network security. Here is a link to a collection of important papers in AI cyber security that can be relevant for the above https://medium.com/@jason_trost/collection-of-deep-learning-cyber-security-research-papers-e1f856f71042
AI Schedule Assistant
In a work setting employees often spend a lot of time coordinating meetings and phone calls. This task can be time-consuming and tedious. Sending emails back and forth, trying to find a convenient time for each person can take time away from more critical duties. This is why I believe you should consider implementing an AI schedule assistant to automate the task of scheduling meetings and appointments within your workplace. The startup X.ai can be a good starting point. However if you need a solution that provides you with more control over the source code and that can be installed on the hardware and OS's that you are already using at your workplace I then highly recommend that you have a look at mycroft and lucida which are both impressive AI virtual assistants.
AI & Regulatory Compliance
In many companies the legal and compliance staff struggle to understand the regulatory requirements that they face within their line of business. As markets evolve, businesses can end up having to comply with thousands of regulations from different regulators. In addition to the above complexities, regulations change and their interpretation changes within the course of time. The good news is that AI technologies are promising to change the way firms manage their compliance and regulatory risk. These solutions use NLP (Natural Language Processing) to parse regulatory text and pattern match it with particular regulatory keywords relevant to the organisation. You could introduce your company to solutions from startups i.e. compliance.ai, such products will enable management to demonstrate compliance to regulators resulting in reduced compliance failures and regulatory fines.
Chatbots for Business
Statistics show that a majority of Apps are used only once and uninstalled. Instead of developing a smartphone app for your workplace why don't you upsell the advantages of building an AI powered chatbot? You could build a chatbot that could live in any major chat application i.e. FB messenger, Slack or WhatsApp. There are a number of services i.e. Chatfuel, Botsify, Flow XO and Beep Bop to host your chatbot. You could train the conversational model using Seq2Seq on tensorflow. Chatbots not only offer a better customer support experience, your company will soon find out that chatbots allow for significant cost savings in their call centers.
AI for Content Generation
In today's workplace there is a need to create content at scale. Business blogging for example helps boost SEO, it drives traffic to your website and helps convert that traffic into leads. Additionally, a business needs to generate press releases, internal memos and legal documents. Although AI won't take over writing assignments completely, it can be used alongside content writers to improve productivity. You can use LSTM RNN to build a content generation platform for your company. Such a platform can help tackle monotonous tasks while improving efficiency.