Latest trends in Artificial Intelligence and Machine Learning?
Before we discuss on the trendy topics for research, let us first clear the air on the differences in terminology. The key question that you need to ask is this: are AI and machine learning the same buzzwords?
No. machine learning (ML) is a subset of artificial intelligence (AI). Also, machine learning plays a role in big data. And deep learning is a part of machine learning; however, these concepts tend to be mentioned interchangeably. Now, what do these concepts entail?
A quick intro
AI or machine intelligence is a wider term compared to machine learning, and AI leverages the use of computers to imitate the cognitive aspects/functions of human beings; act, react and work just like a human
AI is about machines performing routines in a smart fashion that is based on algorithms. On the other hand, machine learning is a subgroup of AI that harnesses the capability of machines to receive data and learn by itself—say modifying algorithms—as and when they know more about the information that they are processing. And this alteration happens based on previous experience. So computers are not explicitly programmed. We use the word “intelligence” because the algorithm is modified to perform better than the previous instance.
Let’s dive in to the key topics that are worth considering for your research from 2018 to 2020.
Topics in AI and topics machine learning
- Merits of neural networks
- Drawing insights from Google brain learning
- How AI is impacting Chinese agricultural landscape?
- Startups and patents: why so many of them these days?
- How algorithms trace out cancerous cells in oncology?
- What’s in store for robotics technology through AI?
- Need for transparency in using algorithms. Understanding and reasoning out the purpose behind it and its machine conclusions.
- Ascertaining genetic issues through facial recognition software.
- How AI is used in drone technology?
- AI helps to excavate insights from patient’s data
- A case study on a patient’s predictive analyses using ML.
- What’s new in edge computing and wearable devices?
- How Amazon and Salesforce and Google and Microsoft streamline their AI capabilities? Can small firms sustain amidst the giants?
- Ways to access and safeguard patient’s data. Overcoming roadblocks.
- Apt candidate for the apt job—are chatbots doing their part?
- Thwarting epidemic outbreaks. ML to the rescue.
- Deep learning networks
- Comparison of the different types of algorithms
- Role of data scientists in data cleansing
- Role of ML in digitalizing electronic health records.
- How to ensure data is reliable before processing it?
- What could possibly go wrong if the algorithms are just not right?
- Identifying theft and fraud patterns
- How machine learning assists surgical robots?
- Patients: what is the prognosis from machine learning?
- How AI in facial recognition devices thwarts crime?
- Applications of ML in CRO: Hiring trial participants, drawing a wider span of data.
- Sanctioning the right loan to the right customer thanks to ML.
- What happens if machine learning falls in the wrong hands?
- Sustainability of self-driving cars in India.
- Effective and alternative methods of dealing with missing variables.
- ML to monitor and report adverse reactions in clinical trials.
- Incorporating “empathy” factor in AI.
- How Tesla uses AI.
- Role of machine learning in clinical diagnosis
- A novel approach to fighting cybercrime is here: machine learning.
In conclusion, write projects that explore the multifaceted role of AI and ML. The above topics are worth considering for your research projects.
