Popular Trends in Social Media and the web in the Current Millennium
It is well known to the current society that social media is absolutely prudent in this era where everything is all web based and any form of knowledge is freely accessible using the internet no matter where you are in this world. There were no shortage of changes to the social media industry last year, which included several new features, consumer preferences and brand opportunities. Looking toward 2018, social media trends will continue to evolve and surprise us. Although there were no direct connection between using the current media to help on the studies on biotechnology, the implicit connection between those two is really significant to the advancement of current day biology advancements.
It is absolutely correct to state that although social medias has its ups and downs, it helps a lot in transferring information and communication between people in this field, whereas also helping current scholars to further improve their education standards by discovering a vast amount of useful knowledge using the internet, as well as communicating with fellow scholars to further understand the ongoing researches and puzzle pieces, so that they could actually have a clear direction on what they are going to in the future.

The World Wide Web (WWW), Google , Gmail and social media platforms
The world wide web is not new to either you or me,and as it’s name suggest it is truly a tool to use by the entire world and widely exposed of information. In our field, biotechnology, or even in any other fields that consist of deep and further learning on a subject, it is truly prudent for accessing information that are not readily prepared in an environment. The most popular system used by the entire population of the world is Google, where it could access its vast storage of knowledge and aid in our biotechnology studies. The further implications to be used by scholars like us is Google scholar,where researchers and scholars from all around the world share their work to further push the limits of the understanding on biotechnology. It is seen to be used by every student now,regarding of courses or level of study. Furthermore, e-mail is also proven to be a high-reliability tool to communicate among lecturers and scholars,as it is counted by modern society as a polite and more proper way to address your problems to your lecturers online. We can also share projects and assignments among acquaintances, to further understand the process of our group works to polish and nurture the outcome of our work to lecturers. Furthermore, we have social media platforms, which, as some suggest to be quite popular nowadays, Facebook, Whatsapp and Instagram to further improve communications among all bodies of people related to biotechnology for the efficiency and accuracy of work done. It is not only a tool of communication, as it is also a really good tool for relation bonding and to make the learning process fun and interesting.

The advancement of Artificial Intelligence and its relation with Biotech
Artificial intelligence (AI) and machine learning (ML) have become ubiquitous in tech start ups, fueled largely by the increasing accessibility and amount of data and strong super computers. Now, if you are a new tech startup, ML or AI capabilities represent your starting floor-plan to enter the industry. Over the past few years, AI and ML have started to peek their heads into the world of biotech, due to an analogous transformation of biotech data.
We are beginning to see partnerships form between Big Pharma and biotech startups that employ AI and ML for drug discovery and other purposes. Positive results have already come out of joint projects, notably the delay in the onset of motor neuron disease in an efficacy study conducted by SITraN on a drug candidate proposed by BenevolentBIO. With these statistic currently shown as above, how do we digest the role of AI and ML now and also in the distant future of biotech?
For one, the field in diagnosis is actually a really good platform for machine-learning and artificial intelligence entities to show their true skills of calculation and tabulation of data. ML techniques can immediately use the gathered results over the years to improve the diagnostics test conducted by mere humans. This means the more diagnostic test are run,the more accurate the test could be. Currently, the most emphasized field of AI in biotechnology is in genetics. A well company that conduct these kinds of experiments is Sophia Genetics, where they intake a biopsy or blood sample from the patient, process the sample, and then analyze the data with their powerful analytical AI algorithms. In Sophia Genetics’ case, the data analysis takes a few days with its platform, rather than several months like the current standard. While speed is clearly a benefit, the long-term advantage is that the machine learning algorithm that’s behind the AI analysis enables the diagnostic process to become smarter with each iteration. Besides genetic analysis, ML techniques can be used in any diagnostic that can be digitized, allowing the algorithm to determine the correct “features” to embed into its final decision-making process. DNAlytics demonstrates another use of ML in diagnostics, using the advanced computations to help diagnose rheumatoid arthritis. Furthermore, AI is also a really good lab assistance with its near to zero-error performance, as well as the accuracy and the ability to work at long hours without rest, making it a truly remarkable secretarial worker. A leading company in this field, Desktop Genetics has created a platform to design gene editing constructs using CRISPR that works through AI. The gene editing process is followed by the operating AI throughout the whole experiment, from selecting proper sgRNA molecules to analyzing the data of the experiment. The ability of AI allows them to perform effectively when constructing CRISPR libraries that might be required by fellow biologists, especially for people who lack experience working with CRISPR-Cas9, this platform is valuable to not only reconstruct the process from designing to conducting an experiment but also to ensure that the guides are as effective as they can be, improving the efficacy of gene editing. For scientists who want quicker and easier data analysis, there are newly introduced platforms focused on using AI to explore many types of genetic data. is an open-source platform on which scientists can analyze data using thousands of different statistical analysis models. While is industry-agnostic, there are a few programs focused specifically on health care and biotech data, reducing the trouble of data processing from healthcare providers.As the amount of data keeps increasing proportional to the flow of time, not all of this data can be used correctly at this moment. These startups are aiming to reduce the bottleneck at data analysis to take advantage of the rich data sets that exist.
Debatably, the most exiting advances within biotechnology field with AI and ML resources is shockingly discovered in the sensitive field of drug discovery. The current impact on drug discovery are immersible to the economics all around the world, especially towards the most focused field of medical advances. It is known that an estimated value of 2.5 billion and an average of 12 years has been spent towards trials of a single drug. However, the solution may be lying in the infinite-potential AI and ML fields where they promised to make drug discovery cheaper and quicker,effectively making the time needed for leading discoveries a fraction of what we are needing now. Several approaches already exist on making these advances happen, presumably on increasing the amount of genetic data and cheap sequencing to approach data from a genetic point of view. This actually salvages the time needed for PhD professors to painstakingly peer into countless of microscope a countless number of times to get the job done. A few companies are taking a structure-based approach to drug discovery, using ML to find small molecules that could provide therapeutic benefits based on known target structures. Lastly, startups like BenevolentBIO use AI to pore over the vast, existing scientific data. With those results, they can reuse previously conducted studies to better inform future experiments and clue researchers into possible missteps in previous trials or even better designations for drugs.With all of the good aspects of AI in biotech, there are a few challenges that could put a damper on progress. Most notably, the large volume of data is often stored in disparate or incompatible mediums, making it difficult to consolidate results and draw upon the entire wealth of data. Furthermore, data privacy is also a concern, particularly for companies using cloud computing to analyze patient-derived data, but at least in the US trailblazers have already jumped this hurdle.
Overall, AI and ML are coming into biotech and are here to stay. What will exactly happen is still up for debate, and AI biotech companies are still being created, with beneficial reason. The future of biotech is being written at this moment. The question is: who is writing it and what are they writing?
1)Snyder, Micheal , March 5 2018, The Robots are Coming : Is AI the Future of Biotech? 29 June 2018